idx int64 1 56k | question stringlengths 15 155 | answer stringlengths 2 29.2k ⌀ | question_cut stringlengths 15 100 | answer_cut stringlengths 2 200 ⌀ | conversation stringlengths 47 29.3k | conversation_cut stringlengths 47 301 |
|---|---|---|---|---|---|---|
13,901 | Why does the Akaike Information Criterion (AIC) sometimes favor an overfitted model? | I don't have an answer for you, but a few things to consider.
First, there are some duplicates rows in the data you are using, so obviously overfitting is not such a big issue in that case.
However, removing duplicate rows won't really solve the weird behavior of AIC
library(tidyverse)
library(splines)
... | Why does the Akaike Information Criterion (AIC) sometimes favor an overfitted model? | I don't have an answer for you, but a few things to consider.
First, there are some duplicates rows in the data you are using, so obviously overfitting is not such a big issue in that case.
However, | Why does the Akaike Information Criterion (AIC) sometimes favor an overfitted model?
I don't have an answer for you, but a few things to consider.
First, there are some duplicates rows in the data you are using, so obviously overfitting is not such a big issue in that case.
However, removing duplicate rows won't reall... | Why does the Akaike Information Criterion (AIC) sometimes favor an overfitted model?
I don't have an answer for you, but a few things to consider.
First, there are some duplicates rows in the data you are using, so obviously overfitting is not such a big issue in that case.
However, |
13,902 | Does KNN have a loss function? | $k$-NN does not have a loss function that can be minimized during training. In fact, this algorithm is not trained at all. The only "training" that happens for $k$-NN, is memorising the data (creating a local copy), so that during prediction you can do a search and majority vote. Technically, no function is fitted to t... | Does KNN have a loss function? | $k$-NN does not have a loss function that can be minimized during training. In fact, this algorithm is not trained at all. The only "training" that happens for $k$-NN, is memorising the data (creating | Does KNN have a loss function?
$k$-NN does not have a loss function that can be minimized during training. In fact, this algorithm is not trained at all. The only "training" that happens for $k$-NN, is memorising the data (creating a local copy), so that during prediction you can do a search and majority vote. Technica... | Does KNN have a loss function?
$k$-NN does not have a loss function that can be minimized during training. In fact, this algorithm is not trained at all. The only "training" that happens for $k$-NN, is memorising the data (creating |
13,903 | Does KNN have a loss function? | As an alternative to the accepted answer:
Every stats algorithm is explicitly or implicitly minimizing some objective, even if there are no parameters or hyperparameters, and even if the minimization is not done iteratively. The kNN is so simple that one does not typically think of it like this, but you can actually wr... | Does KNN have a loss function? | As an alternative to the accepted answer:
Every stats algorithm is explicitly or implicitly minimizing some objective, even if there are no parameters or hyperparameters, and even if the minimization | Does KNN have a loss function?
As an alternative to the accepted answer:
Every stats algorithm is explicitly or implicitly minimizing some objective, even if there are no parameters or hyperparameters, and even if the minimization is not done iteratively. The kNN is so simple that one does not typically think of it lik... | Does KNN have a loss function?
As an alternative to the accepted answer:
Every stats algorithm is explicitly or implicitly minimizing some objective, even if there are no parameters or hyperparameters, and even if the minimization |
13,904 | Is the Gaussian distribution a specific case of the Beta Distribution? | They are both symmetric and more or less bell shaped, but the symmetric beta (whether at 4,4 or at any other specific value) is not actually Gaussian. You can tell this even without looking at the density -- beta distributions are on (0,1) while all Gaussian distributions are on $(-\infty,\infty)$
Let's look a bit more... | Is the Gaussian distribution a specific case of the Beta Distribution? | They are both symmetric and more or less bell shaped, but the symmetric beta (whether at 4,4 or at any other specific value) is not actually Gaussian. You can tell this even without looking at the den | Is the Gaussian distribution a specific case of the Beta Distribution?
They are both symmetric and more or less bell shaped, but the symmetric beta (whether at 4,4 or at any other specific value) is not actually Gaussian. You can tell this even without looking at the density -- beta distributions are on (0,1) while all... | Is the Gaussian distribution a specific case of the Beta Distribution?
They are both symmetric and more or less bell shaped, but the symmetric beta (whether at 4,4 or at any other specific value) is not actually Gaussian. You can tell this even without looking at the den |
13,905 | Covariance functions or kernels - what exactly are they? | In loose terms, a kernel or covariance function $k(x, x^\prime)$ specifies the statistical relationship between two points $x, x^\prime$ in your input space; that is, how markedly a change in the value of the Gaussian Process (GP) at $x$ correlates with a change in the GP at $x^\prime$. In some sense, you can think of ... | Covariance functions or kernels - what exactly are they? | In loose terms, a kernel or covariance function $k(x, x^\prime)$ specifies the statistical relationship between two points $x, x^\prime$ in your input space; that is, how markedly a change in the valu | Covariance functions or kernels - what exactly are they?
In loose terms, a kernel or covariance function $k(x, x^\prime)$ specifies the statistical relationship between two points $x, x^\prime$ in your input space; that is, how markedly a change in the value of the Gaussian Process (GP) at $x$ correlates with a change ... | Covariance functions or kernels - what exactly are they?
In loose terms, a kernel or covariance function $k(x, x^\prime)$ specifies the statistical relationship between two points $x, x^\prime$ in your input space; that is, how markedly a change in the valu |
13,906 | Covariance functions or kernels - what exactly are they? | As @lacerbi suggests a kernel function (or covariance function in a Gaussian Process setting) is essentially a similarity metric, so that the value of the kernel is high if the two input vectors are considered "similar" according to the needs of the application and lower if they are dissimilar. However not all similar... | Covariance functions or kernels - what exactly are they? | As @lacerbi suggests a kernel function (or covariance function in a Gaussian Process setting) is essentially a similarity metric, so that the value of the kernel is high if the two input vectors are c | Covariance functions or kernels - what exactly are they?
As @lacerbi suggests a kernel function (or covariance function in a Gaussian Process setting) is essentially a similarity metric, so that the value of the kernel is high if the two input vectors are considered "similar" according to the needs of the application a... | Covariance functions or kernels - what exactly are they?
As @lacerbi suggests a kernel function (or covariance function in a Gaussian Process setting) is essentially a similarity metric, so that the value of the kernel is high if the two input vectors are c |
13,907 | How to calculate the expected value of a standard normal distribution? | You are almost there,
follow your last step:
$$E[X] = \frac{1}{\sqrt{2\pi}}\int_{-\infty}^{\infty} xe^{\displaystyle\frac{-x^{2}}{2}}\mathrm{d}x\\=-\frac{1}{\sqrt{2\pi}}\int_{-\infty}^{\infty}e^{-x^2/2}d(-\frac{x^2}{2})\\=-\frac{1}{\sqrt{2\pi}}e^{-x^2/2}\mid_{-\infty}^{\infty}\\=0$$.
Or you can directly use the fact th... | How to calculate the expected value of a standard normal distribution? | You are almost there,
follow your last step:
$$E[X] = \frac{1}{\sqrt{2\pi}}\int_{-\infty}^{\infty} xe^{\displaystyle\frac{-x^{2}}{2}}\mathrm{d}x\\=-\frac{1}{\sqrt{2\pi}}\int_{-\infty}^{\infty}e^{-x^2/ | How to calculate the expected value of a standard normal distribution?
You are almost there,
follow your last step:
$$E[X] = \frac{1}{\sqrt{2\pi}}\int_{-\infty}^{\infty} xe^{\displaystyle\frac{-x^{2}}{2}}\mathrm{d}x\\=-\frac{1}{\sqrt{2\pi}}\int_{-\infty}^{\infty}e^{-x^2/2}d(-\frac{x^2}{2})\\=-\frac{1}{\sqrt{2\pi}}e^{-x... | How to calculate the expected value of a standard normal distribution?
You are almost there,
follow your last step:
$$E[X] = \frac{1}{\sqrt{2\pi}}\int_{-\infty}^{\infty} xe^{\displaystyle\frac{-x^{2}}{2}}\mathrm{d}x\\=-\frac{1}{\sqrt{2\pi}}\int_{-\infty}^{\infty}e^{-x^2/ |
13,908 | How to calculate the expected value of a standard normal distribution? | Since you want to learn methods for computing expectations, and you wish to know some simple ways, you will enjoy using the moment generating function (mgf)
$$\phi(t) = E[e^{tX}].$$
The method works especially well when the distribution function or its density are given as exponentials themselves. In this case, you do... | How to calculate the expected value of a standard normal distribution? | Since you want to learn methods for computing expectations, and you wish to know some simple ways, you will enjoy using the moment generating function (mgf)
$$\phi(t) = E[e^{tX}].$$
The method works e | How to calculate the expected value of a standard normal distribution?
Since you want to learn methods for computing expectations, and you wish to know some simple ways, you will enjoy using the moment generating function (mgf)
$$\phi(t) = E[e^{tX}].$$
The method works especially well when the distribution function or ... | How to calculate the expected value of a standard normal distribution?
Since you want to learn methods for computing expectations, and you wish to know some simple ways, you will enjoy using the moment generating function (mgf)
$$\phi(t) = E[e^{tX}].$$
The method works e |
13,909 | How to calculate the expected value of a standard normal distribution? | A more straightforward and general way to calculate these kinds of integrals is by changing of variable:
Suppose your normal distribution has mean $\mu$ and variance $\sigma^2$: $\mathcal{N(\mu, \sigma^2)}$
$$
E(x) = \frac{1}{\sigma\sqrt{2 \pi}} \int x \exp(-\frac{(x-\mu)^2}{2\sigma^2})dx
$$
now by changing the variabl... | How to calculate the expected value of a standard normal distribution? | A more straightforward and general way to calculate these kinds of integrals is by changing of variable:
Suppose your normal distribution has mean $\mu$ and variance $\sigma^2$: $\mathcal{N(\mu, \sigm | How to calculate the expected value of a standard normal distribution?
A more straightforward and general way to calculate these kinds of integrals is by changing of variable:
Suppose your normal distribution has mean $\mu$ and variance $\sigma^2$: $\mathcal{N(\mu, \sigma^2)}$
$$
E(x) = \frac{1}{\sigma\sqrt{2 \pi}} \in... | How to calculate the expected value of a standard normal distribution?
A more straightforward and general way to calculate these kinds of integrals is by changing of variable:
Suppose your normal distribution has mean $\mu$ and variance $\sigma^2$: $\mathcal{N(\mu, \sigm |
13,910 | Julia: Taking stock of how it has been doing | I have switched to Julia, and here are my pragmatic reasons:
It does glue code really well. I have a lot of legacy code in MATLAB, and MATLAB.jl took 5 minutes to install, works perfectly, and has a succinct syntax that makes it natural to use MATLAB functions. Julia also has the same for R, Python, C, Fortran, and ma... | Julia: Taking stock of how it has been doing | I have switched to Julia, and here are my pragmatic reasons:
It does glue code really well. I have a lot of legacy code in MATLAB, and MATLAB.jl took 5 minutes to install, works perfectly, and has a | Julia: Taking stock of how it has been doing
I have switched to Julia, and here are my pragmatic reasons:
It does glue code really well. I have a lot of legacy code in MATLAB, and MATLAB.jl took 5 minutes to install, works perfectly, and has a succinct syntax that makes it natural to use MATLAB functions. Julia also h... | Julia: Taking stock of how it has been doing
I have switched to Julia, and here are my pragmatic reasons:
It does glue code really well. I have a lot of legacy code in MATLAB, and MATLAB.jl took 5 minutes to install, works perfectly, and has a |
13,911 | Julia: Taking stock of how it has been doing | I think "learn X over Y" isn't the right way to formulate the question. In fact, you can learn (at least basics of) both and decide on the right tool depending on concrete task at hand. And since Julia inherited most of its syntax and concepts from other languages, it shoud be really easy to grasp it (as well as Python... | Julia: Taking stock of how it has been doing | I think "learn X over Y" isn't the right way to formulate the question. In fact, you can learn (at least basics of) both and decide on the right tool depending on concrete task at hand. And since Juli | Julia: Taking stock of how it has been doing
I think "learn X over Y" isn't the right way to formulate the question. In fact, you can learn (at least basics of) both and decide on the right tool depending on concrete task at hand. And since Julia inherited most of its syntax and concepts from other languages, it shoud ... | Julia: Taking stock of how it has been doing
I think "learn X over Y" isn't the right way to formulate the question. In fact, you can learn (at least basics of) both and decide on the right tool depending on concrete task at hand. And since Juli |
13,912 | Julia: Taking stock of how it has been doing | (b) What sort of Statistics use-cases would you advise someone to use Julia in
(c) If R is slow at a certain task does it make sense to switch to
Julia or Python?
High dimensional and compute intensive problems.
Multiprocessing. Julia's single node parallel capabilities (@spawnat) are much more convenient than those ... | Julia: Taking stock of how it has been doing | (b) What sort of Statistics use-cases would you advise someone to use Julia in
(c) If R is slow at a certain task does it make sense to switch to
Julia or Python?
High dimensional and compute intensi | Julia: Taking stock of how it has been doing
(b) What sort of Statistics use-cases would you advise someone to use Julia in
(c) If R is slow at a certain task does it make sense to switch to
Julia or Python?
High dimensional and compute intensive problems.
Multiprocessing. Julia's single node parallel capabilities (@... | Julia: Taking stock of how it has been doing
(b) What sort of Statistics use-cases would you advise someone to use Julia in
(c) If R is slow at a certain task does it make sense to switch to
Julia or Python?
High dimensional and compute intensi |
13,913 | What would be an illustrative picture for linear mixed models? | For a talk, I've used the following picture which is based on the sleepstudy dataset from the lme4 package. The idea was to illustrate the difference between independent regression fits from subject-specific data (gray) versus predictions from random-effects models, especially that (1) predicted values from random-effe... | What would be an illustrative picture for linear mixed models? | For a talk, I've used the following picture which is based on the sleepstudy dataset from the lme4 package. The idea was to illustrate the difference between independent regression fits from subject-s | What would be an illustrative picture for linear mixed models?
For a talk, I've used the following picture which is based on the sleepstudy dataset from the lme4 package. The idea was to illustrate the difference between independent regression fits from subject-specific data (gray) versus predictions from random-effect... | What would be an illustrative picture for linear mixed models?
For a talk, I've used the following picture which is based on the sleepstudy dataset from the lme4 package. The idea was to illustrate the difference between independent regression fits from subject-s |
13,914 | What would be an illustrative picture for linear mixed models? | So something not "extremely elegant" but showing random intercepts and slopes too with R. (I guess it would be even cooler if if showed the actual equations also)
N =100; set.seed(123);
x1 = runif(N)*3; readings1 <- 2*x1 + 1.0 + rnorm(N)*.99;
x2 = runif(N)*3; readings2 <- 3*x2 + 1.5 + rnorm(N)*.99;
x3 = runif(N)*3; ... | What would be an illustrative picture for linear mixed models? | So something not "extremely elegant" but showing random intercepts and slopes too with R. (I guess it would be even cooler if if showed the actual equations also)
N =100; set.seed(123);
x1 = runif( | What would be an illustrative picture for linear mixed models?
So something not "extremely elegant" but showing random intercepts and slopes too with R. (I guess it would be even cooler if if showed the actual equations also)
N =100; set.seed(123);
x1 = runif(N)*3; readings1 <- 2*x1 + 1.0 + rnorm(N)*.99;
x2 = runif(... | What would be an illustrative picture for linear mixed models?
So something not "extremely elegant" but showing random intercepts and slopes too with R. (I guess it would be even cooler if if showed the actual equations also)
N =100; set.seed(123);
x1 = runif( |
13,915 | What would be an illustrative picture for linear mixed models? | This graph taken from the Matlab documentation of nlmefit strikes me as one really exemplifying the concept of random intercepts and slopes quite obviously. Probably something showing groups of heteroskedasticity in the residuals of an OLS plot would be also pretty standard but I wouldn't give a "solution". | What would be an illustrative picture for linear mixed models? | This graph taken from the Matlab documentation of nlmefit strikes me as one really exemplifying the concept of random intercepts and slopes quite obviously. Probably something showing groups of hetero | What would be an illustrative picture for linear mixed models?
This graph taken from the Matlab documentation of nlmefit strikes me as one really exemplifying the concept of random intercepts and slopes quite obviously. Probably something showing groups of heteroskedasticity in the residuals of an OLS plot would be als... | What would be an illustrative picture for linear mixed models?
This graph taken from the Matlab documentation of nlmefit strikes me as one really exemplifying the concept of random intercepts and slopes quite obviously. Probably something showing groups of hetero |
13,916 | Logistic regression with binary dependent and independent variables | There is no reason not to do this, but two cautionary thoughts:
Keep careful track during the analysis of which is which. In large projects, it can be easy to get lost, and produce errant results.
If you choose to report regression estimates, rather than odds ratios, make your coding scheme clear in your report, so re... | Logistic regression with binary dependent and independent variables | There is no reason not to do this, but two cautionary thoughts:
Keep careful track during the analysis of which is which. In large projects, it can be easy to get lost, and produce errant results.
If | Logistic regression with binary dependent and independent variables
There is no reason not to do this, but two cautionary thoughts:
Keep careful track during the analysis of which is which. In large projects, it can be easy to get lost, and produce errant results.
If you choose to report regression estimates, rather t... | Logistic regression with binary dependent and independent variables
There is no reason not to do this, but two cautionary thoughts:
Keep careful track during the analysis of which is which. In large projects, it can be easy to get lost, and produce errant results.
If |
13,917 | Logistic regression with binary dependent and independent variables | For, clarity: the term "binary" is usually reserved to 1 vs 0 coding only. More general word suitable for any 2-value coding is "dichotomous". Dichotomous predictors are of course welcome to logistic regression, like to linear regression, and, because they have only 2 values, it makes no difference whether to input the... | Logistic regression with binary dependent and independent variables | For, clarity: the term "binary" is usually reserved to 1 vs 0 coding only. More general word suitable for any 2-value coding is "dichotomous". Dichotomous predictors are of course welcome to logistic | Logistic regression with binary dependent and independent variables
For, clarity: the term "binary" is usually reserved to 1 vs 0 coding only. More general word suitable for any 2-value coding is "dichotomous". Dichotomous predictors are of course welcome to logistic regression, like to linear regression, and, because ... | Logistic regression with binary dependent and independent variables
For, clarity: the term "binary" is usually reserved to 1 vs 0 coding only. More general word suitable for any 2-value coding is "dichotomous". Dichotomous predictors are of course welcome to logistic |
13,918 | Logistic regression with binary dependent and independent variables | Typically it helps interpretation if you code your predictors 0-1, but apart from that (and noting that it is not required), there is nothing wrong with this. There are some other (contingency-table based) approaches, but if I recall correctly, these turn out to be equivalent to (some form of) logistic regression.
So i... | Logistic regression with binary dependent and independent variables | Typically it helps interpretation if you code your predictors 0-1, but apart from that (and noting that it is not required), there is nothing wrong with this. There are some other (contingency-table b | Logistic regression with binary dependent and independent variables
Typically it helps interpretation if you code your predictors 0-1, but apart from that (and noting that it is not required), there is nothing wrong with this. There are some other (contingency-table based) approaches, but if I recall correctly, these t... | Logistic regression with binary dependent and independent variables
Typically it helps interpretation if you code your predictors 0-1, but apart from that (and noting that it is not required), there is nothing wrong with this. There are some other (contingency-table b |
13,919 | Logistic regression with binary dependent and independent variables | In addition, if you have more than two predictors, then it is more likely that there would be a problem of multi-collinearity even for logistic or multiple regression. However, there is no harm to use logistic regression with all binary variables (i.e., coded (0,1)). | Logistic regression with binary dependent and independent variables | In addition, if you have more than two predictors, then it is more likely that there would be a problem of multi-collinearity even for logistic or multiple regression. However, there is no harm to us | Logistic regression with binary dependent and independent variables
In addition, if you have more than two predictors, then it is more likely that there would be a problem of multi-collinearity even for logistic or multiple regression. However, there is no harm to use logistic regression with all binary variables (i.e... | Logistic regression with binary dependent and independent variables
In addition, if you have more than two predictors, then it is more likely that there would be a problem of multi-collinearity even for logistic or multiple regression. However, there is no harm to us |
13,920 | Article about misuse of statistical method in NYTimes | I will answer the first question in detail.
With a fair coin, the chances of
getting 527 or more heads in 1,000
flips is less than 1 in 20, or 5
percent, the conventional cutoff.
For a fair coin the number of heads in 1000 trials follows the binomial distribution with number of trials $n=1000$ and probability $... | Article about misuse of statistical method in NYTimes | I will answer the first question in detail.
With a fair coin, the chances of
getting 527 or more heads in 1,000
flips is less than 1 in 20, or 5
percent, the conventional cutoff.
For a fair co | Article about misuse of statistical method in NYTimes
I will answer the first question in detail.
With a fair coin, the chances of
getting 527 or more heads in 1,000
flips is less than 1 in 20, or 5
percent, the conventional cutoff.
For a fair coin the number of heads in 1000 trials follows the binomial distrib... | Article about misuse of statistical method in NYTimes
I will answer the first question in detail.
With a fair coin, the chances of
getting 527 or more heads in 1,000
flips is less than 1 in 20, or 5
percent, the conventional cutoff.
For a fair co |
13,921 | Why do t-test and ANOVA give different p-values for two-group comparison? | By default the argument var.equal of t.test() equals FALSE.
In lm(), the residuals are supposed to have constant variance.
Thus, by setting var.equal = TRUE in t.test(), you should get the same result.
var.equals indicates whether to treat the two variances as being equal. If TRUE then the pooled variance is used to es... | Why do t-test and ANOVA give different p-values for two-group comparison? | By default the argument var.equal of t.test() equals FALSE.
In lm(), the residuals are supposed to have constant variance.
Thus, by setting var.equal = TRUE in t.test(), you should get the same result | Why do t-test and ANOVA give different p-values for two-group comparison?
By default the argument var.equal of t.test() equals FALSE.
In lm(), the residuals are supposed to have constant variance.
Thus, by setting var.equal = TRUE in t.test(), you should get the same result.
var.equals indicates whether to treat the tw... | Why do t-test and ANOVA give different p-values for two-group comparison?
By default the argument var.equal of t.test() equals FALSE.
In lm(), the residuals are supposed to have constant variance.
Thus, by setting var.equal = TRUE in t.test(), you should get the same result |
13,922 | Should we teach kurtosis in an applied statistics course? If so, how? | Kurtosis is really pretty simple ... and useful. It is simply a measure of outliers, or tails. It has nothing to do with the peak whatsoever - that definition must be abandoned.
Here is a data set:
0, 3, 4, 1, 2, 3, 0, 2, 1, 3, 2, 0, 2, 2, 3, 2, 5, 2, 3, 999
Notice that '999' is an outlier.
Here are the $z^4$ values ... | Should we teach kurtosis in an applied statistics course? If so, how? | Kurtosis is really pretty simple ... and useful. It is simply a measure of outliers, or tails. It has nothing to do with the peak whatsoever - that definition must be abandoned.
Here is a data set:
| Should we teach kurtosis in an applied statistics course? If so, how?
Kurtosis is really pretty simple ... and useful. It is simply a measure of outliers, or tails. It has nothing to do with the peak whatsoever - that definition must be abandoned.
Here is a data set:
0, 3, 4, 1, 2, 3, 0, 2, 1, 3, 2, 0, 2, 2, 3, 2, 5,... | Should we teach kurtosis in an applied statistics course? If so, how?
Kurtosis is really pretty simple ... and useful. It is simply a measure of outliers, or tails. It has nothing to do with the peak whatsoever - that definition must be abandoned.
Here is a data set:
|
13,923 | Should we teach kurtosis in an applied statistics course? If so, how? | While the question is somewhat vague, it is interesting. At what levels is kurtosis taught? I remember it being mentioned in a (master's level) course in linear models (long time ago, based on first edition of Seber's book). It was not an important topic, but it enters in topics like studying the (lack of) robustnes... | Should we teach kurtosis in an applied statistics course? If so, how? | While the question is somewhat vague, it is interesting. At what levels is kurtosis taught? I remember it being mentioned in a (master's level) course in linear models (long time ago, based on first | Should we teach kurtosis in an applied statistics course? If so, how?
While the question is somewhat vague, it is interesting. At what levels is kurtosis taught? I remember it being mentioned in a (master's level) course in linear models (long time ago, based on first edition of Seber's book). It was not an importan... | Should we teach kurtosis in an applied statistics course? If so, how?
While the question is somewhat vague, it is interesting. At what levels is kurtosis taught? I remember it being mentioned in a (master's level) course in linear models (long time ago, based on first |
13,924 | Should we teach kurtosis in an applied statistics course? If so, how? | I my opinion, the skewness coefficient is useful to motivate the terms: positively skewed and negatively skewed. But, that is where it stops, if your goal is to assess normality. Classical measures of skewness and kurtosis often fail to capture various types of deviation away from normality. I usually advocate to my st... | Should we teach kurtosis in an applied statistics course? If so, how? | I my opinion, the skewness coefficient is useful to motivate the terms: positively skewed and negatively skewed. But, that is where it stops, if your goal is to assess normality. Classical measures of | Should we teach kurtosis in an applied statistics course? If so, how?
I my opinion, the skewness coefficient is useful to motivate the terms: positively skewed and negatively skewed. But, that is where it stops, if your goal is to assess normality. Classical measures of skewness and kurtosis often fail to capture vario... | Should we teach kurtosis in an applied statistics course? If so, how?
I my opinion, the skewness coefficient is useful to motivate the terms: positively skewed and negatively skewed. But, that is where it stops, if your goal is to assess normality. Classical measures of |
13,925 | Should we teach kurtosis in an applied statistics course? If so, how? | Depending on how applied the course is, the question of accuracy of estimates might come up. The accuracy of the variance estimate depends strongly on kurtosis. The reason this happens is that with high kurtosis, the distribution allows rare, extreme potentially observable data. Thus the data-generating process will pr... | Should we teach kurtosis in an applied statistics course? If so, how? | Depending on how applied the course is, the question of accuracy of estimates might come up. The accuracy of the variance estimate depends strongly on kurtosis. The reason this happens is that with hi | Should we teach kurtosis in an applied statistics course? If so, how?
Depending on how applied the course is, the question of accuracy of estimates might come up. The accuracy of the variance estimate depends strongly on kurtosis. The reason this happens is that with high kurtosis, the distribution allows rare, extreme... | Should we teach kurtosis in an applied statistics course? If so, how?
Depending on how applied the course is, the question of accuracy of estimates might come up. The accuracy of the variance estimate depends strongly on kurtosis. The reason this happens is that with hi |
13,926 | Should we teach kurtosis in an applied statistics course? If so, how? | Frankly, I don't understand why people want to complicate simple things. Why not just show the definition (stolen from Wikipedia):
$$\operatorname{Kurt}[X] = \operatorname{E}\left[\left(\frac{X - \mu}{\sigma}\right)^4\right] = \frac{\mu_4}{\sigma^4} = \frac{\operatorname{E}[(X-\mu)^4]}{(\operatorname{E}[(X-\mu)^2])^2},... | Should we teach kurtosis in an applied statistics course? If so, how? | Frankly, I don't understand why people want to complicate simple things. Why not just show the definition (stolen from Wikipedia):
$$\operatorname{Kurt}[X] = \operatorname{E}\left[\left(\frac{X - \mu} | Should we teach kurtosis in an applied statistics course? If so, how?
Frankly, I don't understand why people want to complicate simple things. Why not just show the definition (stolen from Wikipedia):
$$\operatorname{Kurt}[X] = \operatorname{E}\left[\left(\frac{X - \mu}{\sigma}\right)^4\right] = \frac{\mu_4}{\sigma^4} ... | Should we teach kurtosis in an applied statistics course? If so, how?
Frankly, I don't understand why people want to complicate simple things. Why not just show the definition (stolen from Wikipedia):
$$\operatorname{Kurt}[X] = \operatorname{E}\left[\left(\frac{X - \mu} |
13,927 | PCA and exploratory Factor Analysis on the same dataset: differences and similarities; factor model vs PCA | Both models - principal-component and common-factor - are similar straightforward linear regressional models predicting observed variables by latent variables. Let us have centered variables V1 V2 ... Vp and we chose to extract 2 components/factors FI and FII. Then the model is the system of equations:
$V_1 = a_{1I}F_I... | PCA and exploratory Factor Analysis on the same dataset: differences and similarities; factor model | Both models - principal-component and common-factor - are similar straightforward linear regressional models predicting observed variables by latent variables. Let us have centered variables V1 V2 ... | PCA and exploratory Factor Analysis on the same dataset: differences and similarities; factor model vs PCA
Both models - principal-component and common-factor - are similar straightforward linear regressional models predicting observed variables by latent variables. Let us have centered variables V1 V2 ... Vp and we ch... | PCA and exploratory Factor Analysis on the same dataset: differences and similarities; factor model
Both models - principal-component and common-factor - are similar straightforward linear regressional models predicting observed variables by latent variables. Let us have centered variables V1 V2 ... |
13,928 | PCA and exploratory Factor Analysis on the same dataset: differences and similarities; factor model vs PCA | I provided my own account of the similarities and differences between PCA and FA in the following thread: Is there any good reason to use PCA instead of EFA? Also, can PCA be a substitute for factor analysis?
Note that my account is somewhat different from the one by @ttnphns (as presented in his answer above). My mai... | PCA and exploratory Factor Analysis on the same dataset: differences and similarities; factor model | I provided my own account of the similarities and differences between PCA and FA in the following thread: Is there any good reason to use PCA instead of EFA? Also, can PCA be a substitute for factor | PCA and exploratory Factor Analysis on the same dataset: differences and similarities; factor model vs PCA
I provided my own account of the similarities and differences between PCA and FA in the following thread: Is there any good reason to use PCA instead of EFA? Also, can PCA be a substitute for factor analysis?
Not... | PCA and exploratory Factor Analysis on the same dataset: differences and similarities; factor model
I provided my own account of the similarities and differences between PCA and FA in the following thread: Is there any good reason to use PCA instead of EFA? Also, can PCA be a substitute for factor |
13,929 | Modern neural networks that build their own topology | The implicit question here is how can you determine the topology/structure of a neural network or machine learning model so that the model is "of the right size" and not overfitting/underfitting.
Since cascade correlation back in 1990, there has been a whole host of methods for doing this now, many of them with much ... | Modern neural networks that build their own topology | The implicit question here is how can you determine the topology/structure of a neural network or machine learning model so that the model is "of the right size" and not overfitting/underfitting.
Si | Modern neural networks that build their own topology
The implicit question here is how can you determine the topology/structure of a neural network or machine learning model so that the model is "of the right size" and not overfitting/underfitting.
Since cascade correlation back in 1990, there has been a whole host o... | Modern neural networks that build their own topology
The implicit question here is how can you determine the topology/structure of a neural network or machine learning model so that the model is "of the right size" and not overfitting/underfitting.
Si |
13,930 | Modern neural networks that build their own topology | How about NeuroEvolution of Augmenting Topologies (NEAT) http://www.cs.ucf.edu/~kstanley/neat.html
It seems to work for simple problems, but is INCREDIBLY slow to converge. | Modern neural networks that build their own topology | How about NeuroEvolution of Augmenting Topologies (NEAT) http://www.cs.ucf.edu/~kstanley/neat.html
It seems to work for simple problems, but is INCREDIBLY slow to converge. | Modern neural networks that build their own topology
How about NeuroEvolution of Augmenting Topologies (NEAT) http://www.cs.ucf.edu/~kstanley/neat.html
It seems to work for simple problems, but is INCREDIBLY slow to converge. | Modern neural networks that build their own topology
How about NeuroEvolution of Augmenting Topologies (NEAT) http://www.cs.ucf.edu/~kstanley/neat.html
It seems to work for simple problems, but is INCREDIBLY slow to converge. |
13,931 | Modern neural networks that build their own topology | As I understand the top of the art today is" Unsupervised Feature Learning and Deep Learning". at the nutshell: the network is being trained in unsupervised manner, each layer at a time:
http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial
http://www.youtube.com/watch?v=ZmNOAtZIgIk&feature=player_embedded | Modern neural networks that build their own topology | As I understand the top of the art today is" Unsupervised Feature Learning and Deep Learning". at the nutshell: the network is being trained in unsupervised manner, each layer at a time:
http://ufldl | Modern neural networks that build their own topology
As I understand the top of the art today is" Unsupervised Feature Learning and Deep Learning". at the nutshell: the network is being trained in unsupervised manner, each layer at a time:
http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial
http://www.youtube.com/... | Modern neural networks that build their own topology
As I understand the top of the art today is" Unsupervised Feature Learning and Deep Learning". at the nutshell: the network is being trained in unsupervised manner, each layer at a time:
http://ufldl |
13,932 | Modern neural networks that build their own topology | There's already been a mention of NEAT (Neural Evolution with Augmenting Topologies). There are advances on this including speciation and HyperNEAT. HyperNEAT uses a 'meta' network to optimise the weighting of a fully connected phenotype. This gives a network 'spacial awareness' which is invaluable in image recognition... | Modern neural networks that build their own topology | There's already been a mention of NEAT (Neural Evolution with Augmenting Topologies). There are advances on this including speciation and HyperNEAT. HyperNEAT uses a 'meta' network to optimise the wei | Modern neural networks that build their own topology
There's already been a mention of NEAT (Neural Evolution with Augmenting Topologies). There are advances on this including speciation and HyperNEAT. HyperNEAT uses a 'meta' network to optimise the weighting of a fully connected phenotype. This gives a network 'spacia... | Modern neural networks that build their own topology
There's already been a mention of NEAT (Neural Evolution with Augmenting Topologies). There are advances on this including speciation and HyperNEAT. HyperNEAT uses a 'meta' network to optimise the wei |
13,933 | Modern neural networks that build their own topology | There is a somewhat recent paper on this topic:
R. P. Adams, H. Wallach, and Zoubin Ghahramani. Learning the structure of deep sparse graphical models.
This is a bit outside the usual neural network community and more on the machine learning side.
The paper uses non-parametric Bayesian inference on the network structur... | Modern neural networks that build their own topology | There is a somewhat recent paper on this topic:
R. P. Adams, H. Wallach, and Zoubin Ghahramani. Learning the structure of deep sparse graphical models.
This is a bit outside the usual neural network c | Modern neural networks that build their own topology
There is a somewhat recent paper on this topic:
R. P. Adams, H. Wallach, and Zoubin Ghahramani. Learning the structure of deep sparse graphical models.
This is a bit outside the usual neural network community and more on the machine learning side.
The paper uses non-... | Modern neural networks that build their own topology
There is a somewhat recent paper on this topic:
R. P. Adams, H. Wallach, and Zoubin Ghahramani. Learning the structure of deep sparse graphical models.
This is a bit outside the usual neural network c |
13,934 | Which one is the null hypothesis? Conflict between science theory, logic and statistics? | In statistics there are tests of equivalence as well as the more common test the Null and decide if sufficient evidence against it. The equivalence test turn this on its head and posits that effects are different as the Null and we determine if there is sufficient evidence against this Null.
I'm not clear on your drug ... | Which one is the null hypothesis? Conflict between science theory, logic and statistics? | In statistics there are tests of equivalence as well as the more common test the Null and decide if sufficient evidence against it. The equivalence test turn this on its head and posits that effects a | Which one is the null hypothesis? Conflict between science theory, logic and statistics?
In statistics there are tests of equivalence as well as the more common test the Null and decide if sufficient evidence against it. The equivalence test turn this on its head and posits that effects are different as the Null and we... | Which one is the null hypothesis? Conflict between science theory, logic and statistics?
In statistics there are tests of equivalence as well as the more common test the Null and decide if sufficient evidence against it. The equivalence test turn this on its head and posits that effects a |
13,935 | Which one is the null hypothesis? Conflict between science theory, logic and statistics? | I think this is another case where frequentist statistics can't give a direct answer to the question you actually want to ask, and so answers a (no so) subtly different question, and it is easy to misinterpret this as a direct answer to the question you actually wanted to ask.
What we would really like to ask is normal... | Which one is the null hypothesis? Conflict between science theory, logic and statistics? | I think this is another case where frequentist statistics can't give a direct answer to the question you actually want to ask, and so answers a (no so) subtly different question, and it is easy to mis | Which one is the null hypothesis? Conflict between science theory, logic and statistics?
I think this is another case where frequentist statistics can't give a direct answer to the question you actually want to ask, and so answers a (no so) subtly different question, and it is easy to misinterpret this as a direct answ... | Which one is the null hypothesis? Conflict between science theory, logic and statistics?
I think this is another case where frequentist statistics can't give a direct answer to the question you actually want to ask, and so answers a (no so) subtly different question, and it is easy to mis |
13,936 | Which one is the null hypothesis? Conflict between science theory, logic and statistics? | To add to Gavin's answer, a couple of things:
First, I've heard this idea that propositions can only be falsified, but never proven. Could you post a link to a discussion of this, because with our wording here it doesn't seem to hold up very well - if X is a proposition, then not(X) is a proposition too. If disprovin... | Which one is the null hypothesis? Conflict between science theory, logic and statistics? | To add to Gavin's answer, a couple of things:
First, I've heard this idea that propositions can only be falsified, but never proven. Could you post a link to a discussion of this, because with our wo | Which one is the null hypothesis? Conflict between science theory, logic and statistics?
To add to Gavin's answer, a couple of things:
First, I've heard this idea that propositions can only be falsified, but never proven. Could you post a link to a discussion of this, because with our wording here it doesn't seem to h... | Which one is the null hypothesis? Conflict between science theory, logic and statistics?
To add to Gavin's answer, a couple of things:
First, I've heard this idea that propositions can only be falsified, but never proven. Could you post a link to a discussion of this, because with our wo |
13,937 | Which one is the null hypothesis? Conflict between science theory, logic and statistics? | You are right that, in a sense, frequentist hypothesis testing has it backwards. I'm not saying that that approach is wrong, but rather that the results are often not designed to answer the questions that the researcher is most interested in. If you want a technique more similar to the scientific method, try Bayesian... | Which one is the null hypothesis? Conflict between science theory, logic and statistics? | You are right that, in a sense, frequentist hypothesis testing has it backwards. I'm not saying that that approach is wrong, but rather that the results are often not designed to answer the questions | Which one is the null hypothesis? Conflict between science theory, logic and statistics?
You are right that, in a sense, frequentist hypothesis testing has it backwards. I'm not saying that that approach is wrong, but rather that the results are often not designed to answer the questions that the researcher is most in... | Which one is the null hypothesis? Conflict between science theory, logic and statistics?
You are right that, in a sense, frequentist hypothesis testing has it backwards. I'm not saying that that approach is wrong, but rather that the results are often not designed to answer the questions |
13,938 | Which one is the null hypothesis? Conflict between science theory, logic and statistics? | I think you've got a fundamental error here (not that the whole area of hypothesis testing is clear!) but you say the alternative is what we try to prove. But this is not right. We attempt to reject (falsify) the null.
If the results we obtain would be very unlikely if the null were true, we reject the null.
Now, as o... | Which one is the null hypothesis? Conflict between science theory, logic and statistics? | I think you've got a fundamental error here (not that the whole area of hypothesis testing is clear!) but you say the alternative is what we try to prove. But this is not right. We attempt to reject ( | Which one is the null hypothesis? Conflict between science theory, logic and statistics?
I think you've got a fundamental error here (not that the whole area of hypothesis testing is clear!) but you say the alternative is what we try to prove. But this is not right. We attempt to reject (falsify) the null.
If the resu... | Which one is the null hypothesis? Conflict between science theory, logic and statistics?
I think you've got a fundamental error here (not that the whole area of hypothesis testing is clear!) but you say the alternative is what we try to prove. But this is not right. We attempt to reject ( |
13,939 | Which one is the null hypothesis? Conflict between science theory, logic and statistics? | If I'm understanding you correctly, you're in agreement with the late, great Paul Meehl. See
Meehl, P.E. (1967). Theory-testing in psychology and physics: A methodological paradox. Philosophy of Science, 34:103-115. | Which one is the null hypothesis? Conflict between science theory, logic and statistics? | If I'm understanding you correctly, you're in agreement with the late, great Paul Meehl. See
Meehl, P.E. (1967). Theory-testing in psychology and physics: A methodological paradox. Philosophy of Scie | Which one is the null hypothesis? Conflict between science theory, logic and statistics?
If I'm understanding you correctly, you're in agreement with the late, great Paul Meehl. See
Meehl, P.E. (1967). Theory-testing in psychology and physics: A methodological paradox. Philosophy of Science, 34:103-115. | Which one is the null hypothesis? Conflict between science theory, logic and statistics?
If I'm understanding you correctly, you're in agreement with the late, great Paul Meehl. See
Meehl, P.E. (1967). Theory-testing in psychology and physics: A methodological paradox. Philosophy of Scie |
13,940 | Which one is the null hypothesis? Conflict between science theory, logic and statistics? | I'll expand on the mention of Paul Meehl by @Doc:
1) Testing the opposite of your research hypothesis as the null hypothesis makes it so you can only affirm the consequent which is a "formally invalid" argument. The conclusions do not necessarily follow from the premise.
If Bill Gates owns Fort Knox, then he is rich.
... | Which one is the null hypothesis? Conflict between science theory, logic and statistics? | I'll expand on the mention of Paul Meehl by @Doc:
1) Testing the opposite of your research hypothesis as the null hypothesis makes it so you can only affirm the consequent which is a "formally invalid | Which one is the null hypothesis? Conflict between science theory, logic and statistics?
I'll expand on the mention of Paul Meehl by @Doc:
1) Testing the opposite of your research hypothesis as the null hypothesis makes it so you can only affirm the consequent which is a "formally invalid" argument. The conclusions do ... | Which one is the null hypothesis? Conflict between science theory, logic and statistics?
I'll expand on the mention of Paul Meehl by @Doc:
1) Testing the opposite of your research hypothesis as the null hypothesis makes it so you can only affirm the consequent which is a "formally invalid |
13,941 | Which one is the null hypothesis? Conflict between science theory, logic and statistics? | Isn't all statistics premised on the assumption that nothing is certain in the natural world (as distinct from the man-made world of games &c). In other words, the only way we can get near to understanding it is by measuring the probability that one thing correlates with another and this varies between 0 and 1 but can... | Which one is the null hypothesis? Conflict between science theory, logic and statistics? | Isn't all statistics premised on the assumption that nothing is certain in the natural world (as distinct from the man-made world of games &c). In other words, the only way we can get near to underst | Which one is the null hypothesis? Conflict between science theory, logic and statistics?
Isn't all statistics premised on the assumption that nothing is certain in the natural world (as distinct from the man-made world of games &c). In other words, the only way we can get near to understanding it is by measuring the p... | Which one is the null hypothesis? Conflict between science theory, logic and statistics?
Isn't all statistics premised on the assumption that nothing is certain in the natural world (as distinct from the man-made world of games &c). In other words, the only way we can get near to underst |
13,942 | Which one is the null hypothesis? Conflict between science theory, logic and statistics? | I think the problem is in the word 'true'. The reality of the natural world is innately un-knowable as it's infinitely complex and infinitely variable over time, so 'truth' applied to nature is always conditional. All we can do is try to find levels of probable correspondence between variables by repeated experiment.... | Which one is the null hypothesis? Conflict between science theory, logic and statistics? | I think the problem is in the word 'true'. The reality of the natural world is innately un-knowable as it's infinitely complex and infinitely variable over time, so 'truth' applied to nature is alway | Which one is the null hypothesis? Conflict between science theory, logic and statistics?
I think the problem is in the word 'true'. The reality of the natural world is innately un-knowable as it's infinitely complex and infinitely variable over time, so 'truth' applied to nature is always conditional. All we can do i... | Which one is the null hypothesis? Conflict between science theory, logic and statistics?
I think the problem is in the word 'true'. The reality of the natural world is innately un-knowable as it's infinitely complex and infinitely variable over time, so 'truth' applied to nature is alway |
13,943 | Which one is the null hypothesis? Conflict between science theory, logic and statistics? | We must select null hypothesis the one which we want to reject.
Because in our hypothesis testing scenario, there is a critical region, if the region under hypothesis come in critical region , we reject the hypothesis otherwise we accept the hypothesis.
So suppose we select the null hypothesis , the one we want to acce... | Which one is the null hypothesis? Conflict between science theory, logic and statistics? | We must select null hypothesis the one which we want to reject.
Because in our hypothesis testing scenario, there is a critical region, if the region under hypothesis come in critical region , we reje | Which one is the null hypothesis? Conflict between science theory, logic and statistics?
We must select null hypothesis the one which we want to reject.
Because in our hypothesis testing scenario, there is a critical region, if the region under hypothesis come in critical region , we reject the hypothesis otherwise we ... | Which one is the null hypothesis? Conflict between science theory, logic and statistics?
We must select null hypothesis the one which we want to reject.
Because in our hypothesis testing scenario, there is a critical region, if the region under hypothesis come in critical region , we reje |
13,944 | Within the frequentist "school of thought" how are beliefs updated? | If you're representing beliefs coherently with numbers you're Bayesian by definition. There are at least 46656 different kinds of Bayesian (counted here: http://fitelson.org/probability/good_bayes.pdf) but "quantitatively updating beliefs" is the one thing that unites them; if you do that, you're in the Bayesian club. ... | Within the frequentist "school of thought" how are beliefs updated? | If you're representing beliefs coherently with numbers you're Bayesian by definition. There are at least 46656 different kinds of Bayesian (counted here: http://fitelson.org/probability/good_bayes.pdf | Within the frequentist "school of thought" how are beliefs updated?
If you're representing beliefs coherently with numbers you're Bayesian by definition. There are at least 46656 different kinds of Bayesian (counted here: http://fitelson.org/probability/good_bayes.pdf) but "quantitatively updating beliefs" is the one t... | Within the frequentist "school of thought" how are beliefs updated?
If you're representing beliefs coherently with numbers you're Bayesian by definition. There are at least 46656 different kinds of Bayesian (counted here: http://fitelson.org/probability/good_bayes.pdf |
13,945 | Within the frequentist "school of thought" how are beliefs updated? | Disclaimer: I have a Bayesian bias.
The purpose of frequentist hypothesis testing is to reject the null hypothesis: that is not the same as proving the alternative hypothesis. Such experiments don't give you the "evidence that $H$ is true", even if you can hear people making claims like this. The $p$-value is the proba... | Within the frequentist "school of thought" how are beliefs updated? | Disclaimer: I have a Bayesian bias.
The purpose of frequentist hypothesis testing is to reject the null hypothesis: that is not the same as proving the alternative hypothesis. Such experiments don't g | Within the frequentist "school of thought" how are beliefs updated?
Disclaimer: I have a Bayesian bias.
The purpose of frequentist hypothesis testing is to reject the null hypothesis: that is not the same as proving the alternative hypothesis. Such experiments don't give you the "evidence that $H$ is true", even if you... | Within the frequentist "school of thought" how are beliefs updated?
Disclaimer: I have a Bayesian bias.
The purpose of frequentist hypothesis testing is to reject the null hypothesis: that is not the same as proving the alternative hypothesis. Such experiments don't g |
13,946 | Within the frequentist "school of thought" how are beliefs updated? | No there is not a formal method that frequentists follow to update their beliefs. My very-abridged explanation for why this is the case is as follows, and focuses just on frequentist testing methods. We can regard hypothesis testing as addressing the question: Given the assumed probability model, is the data (statistic... | Within the frequentist "school of thought" how are beliefs updated? | No there is not a formal method that frequentists follow to update their beliefs. My very-abridged explanation for why this is the case is as follows, and focuses just on frequentist testing methods. | Within the frequentist "school of thought" how are beliefs updated?
No there is not a formal method that frequentists follow to update their beliefs. My very-abridged explanation for why this is the case is as follows, and focuses just on frequentist testing methods. We can regard hypothesis testing as addressing the q... | Within the frequentist "school of thought" how are beliefs updated?
No there is not a formal method that frequentists follow to update their beliefs. My very-abridged explanation for why this is the case is as follows, and focuses just on frequentist testing methods. |
13,947 | Can any one explain why dot product is used in neural network and what is the intitutive thought of dot product | Dot products describe part of how neural nets work, conceptually. I'll describe the concept first using scalars, and then show how this can be re-written using the dot product.
Let's take a look at a single unit in a typical neural net. It receives inputs $\{x_1, \dots, x_n\}$ from other units, and produces an output $... | Can any one explain why dot product is used in neural network and what is the intitutive thought of | Dot products describe part of how neural nets work, conceptually. I'll describe the concept first using scalars, and then show how this can be re-written using the dot product.
Let's take a look at a | Can any one explain why dot product is used in neural network and what is the intitutive thought of dot product
Dot products describe part of how neural nets work, conceptually. I'll describe the concept first using scalars, and then show how this can be re-written using the dot product.
Let's take a look at a single u... | Can any one explain why dot product is used in neural network and what is the intitutive thought of
Dot products describe part of how neural nets work, conceptually. I'll describe the concept first using scalars, and then show how this can be re-written using the dot product.
Let's take a look at a |
13,948 | Can any one explain why dot product is used in neural network and what is the intitutive thought of dot product | To answer this question we need to go back to one of the earliest neural networks the Rosenblatt’s perceptron where using vectors and the property of dot product to split hyperplanes of feature vectors were first used.
This may be familiar to many, but it for some a refresher may help
Q. What does a vector mean?
A Vect... | Can any one explain why dot product is used in neural network and what is the intitutive thought of | To answer this question we need to go back to one of the earliest neural networks the Rosenblatt’s perceptron where using vectors and the property of dot product to split hyperplanes of feature vector | Can any one explain why dot product is used in neural network and what is the intitutive thought of dot product
To answer this question we need to go back to one of the earliest neural networks the Rosenblatt’s perceptron where using vectors and the property of dot product to split hyperplanes of feature vectors were f... | Can any one explain why dot product is used in neural network and what is the intitutive thought of
To answer this question we need to go back to one of the earliest neural networks the Rosenblatt’s perceptron where using vectors and the property of dot product to split hyperplanes of feature vector |
13,949 | Can any one explain why dot product is used in neural network and what is the intitutive thought of dot product | The reason we use dot products is because lots of things are lines.
One way of seeing it is that the use of dot product in a neural network originally came
from the idea of using dot product in linear regression.
The most frequently used definition of a line is $y = ax+b$. But this is the same as saying $b = y-ax$, ... | Can any one explain why dot product is used in neural network and what is the intitutive thought of | The reason we use dot products is because lots of things are lines.
One way of seeing it is that the use of dot product in a neural network originally came
from the idea of using dot product in line | Can any one explain why dot product is used in neural network and what is the intitutive thought of dot product
The reason we use dot products is because lots of things are lines.
One way of seeing it is that the use of dot product in a neural network originally came
from the idea of using dot product in linear regre... | Can any one explain why dot product is used in neural network and what is the intitutive thought of
The reason we use dot products is because lots of things are lines.
One way of seeing it is that the use of dot product in a neural network originally came
from the idea of using dot product in line |
13,950 | Can any one explain why dot product is used in neural network and what is the intitutive thought of dot product | It's very rough and imprecise, but I think of the dot product between two matrices or vectors as: "how much are they pulling in the same direction".
If the dot product is 0, they are pulling at a 90 degree angle. If the dot product is positive, then are pulling in the same general direction. If the dot product is negat... | Can any one explain why dot product is used in neural network and what is the intitutive thought of | It's very rough and imprecise, but I think of the dot product between two matrices or vectors as: "how much are they pulling in the same direction".
If the dot product is 0, they are pulling at a 90 d | Can any one explain why dot product is used in neural network and what is the intitutive thought of dot product
It's very rough and imprecise, but I think of the dot product between two matrices or vectors as: "how much are they pulling in the same direction".
If the dot product is 0, they are pulling at a 90 degree an... | Can any one explain why dot product is used in neural network and what is the intitutive thought of
It's very rough and imprecise, but I think of the dot product between two matrices or vectors as: "how much are they pulling in the same direction".
If the dot product is 0, they are pulling at a 90 d |
13,951 | R: calculate p-value given Chi Squared and Degrees of Freedom | In applied statistics, chisquared test statistics arise as sums of squared residuals, or from sums of squared effects or from log-likelihood differences. In all of these applications, the aim is to test whether some vector parameter is zero vs the alternative that it is non-zero and the chisquare statistic is related t... | R: calculate p-value given Chi Squared and Degrees of Freedom | In applied statistics, chisquared test statistics arise as sums of squared residuals, or from sums of squared effects or from log-likelihood differences. In all of these applications, the aim is to te | R: calculate p-value given Chi Squared and Degrees of Freedom
In applied statistics, chisquared test statistics arise as sums of squared residuals, or from sums of squared effects or from log-likelihood differences. In all of these applications, the aim is to test whether some vector parameter is zero vs the alternativ... | R: calculate p-value given Chi Squared and Degrees of Freedom
In applied statistics, chisquared test statistics arise as sums of squared residuals, or from sums of squared effects or from log-likelihood differences. In all of these applications, the aim is to te |
13,952 | R: calculate p-value given Chi Squared and Degrees of Freedom | R has a suite of probability functions for density or mass in the form d* (e.g., dbeta, dchisq), and distribution in the form p* (e.g., pf, pgamma). You might wish to start there. | R: calculate p-value given Chi Squared and Degrees of Freedom | R has a suite of probability functions for density or mass in the form d* (e.g., dbeta, dchisq), and distribution in the form p* (e.g., pf, pgamma). You might wish to start there. | R: calculate p-value given Chi Squared and Degrees of Freedom
R has a suite of probability functions for density or mass in the form d* (e.g., dbeta, dchisq), and distribution in the form p* (e.g., pf, pgamma). You might wish to start there. | R: calculate p-value given Chi Squared and Degrees of Freedom
R has a suite of probability functions for density or mass in the form d* (e.g., dbeta, dchisq), and distribution in the form p* (e.g., pf, pgamma). You might wish to start there. |
13,953 | R: calculate p-value given Chi Squared and Degrees of Freedom | Yes, it is possible to calculate the chi-square value for a given p-value (p) and degrees of freedom (df). Below is how to go about it:
For the sake of verification, I first calculate p for a given chi-square value = 1.1 and df=1:
Solution:
pchisq(1.1, df=1, lower.tail=FALSE)# the answer is p=0.2942661
Now, to go backw... | R: calculate p-value given Chi Squared and Degrees of Freedom | Yes, it is possible to calculate the chi-square value for a given p-value (p) and degrees of freedom (df). Below is how to go about it:
For the sake of verification, I first calculate p for a given ch | R: calculate p-value given Chi Squared and Degrees of Freedom
Yes, it is possible to calculate the chi-square value for a given p-value (p) and degrees of freedom (df). Below is how to go about it:
For the sake of verification, I first calculate p for a given chi-square value = 1.1 and df=1:
Solution:
pchisq(1.1, df=1,... | R: calculate p-value given Chi Squared and Degrees of Freedom
Yes, it is possible to calculate the chi-square value for a given p-value (p) and degrees of freedom (df). Below is how to go about it:
For the sake of verification, I first calculate p for a given ch |
13,954 | R: calculate p-value given Chi Squared and Degrees of Freedom | Try,
pchisq(chi,df)
in your example,
pchisq(15,2)
[1] 0.9994469 | R: calculate p-value given Chi Squared and Degrees of Freedom | Try,
pchisq(chi,df)
in your example,
pchisq(15,2)
[1] 0.9994469 | R: calculate p-value given Chi Squared and Degrees of Freedom
Try,
pchisq(chi,df)
in your example,
pchisq(15,2)
[1] 0.9994469 | R: calculate p-value given Chi Squared and Degrees of Freedom
Try,
pchisq(chi,df)
in your example,
pchisq(15,2)
[1] 0.9994469 |
13,955 | Utility of the Frisch-Waugh theorem | Consider the fixed effects panel data model, also known as Least Squares Dummy Variables (LSDV) model.
$b_{LSDV}$ can be computed by directly applying OLS to the model $$y=X\beta+D\alpha+\epsilon,$$
where $D$ is a $NT\times N$ matrix of dummies and $\alpha$ represent the individual-specific fixed effects.
Another way ... | Utility of the Frisch-Waugh theorem | Consider the fixed effects panel data model, also known as Least Squares Dummy Variables (LSDV) model.
$b_{LSDV}$ can be computed by directly applying OLS to the model $$y=X\beta+D\alpha+\epsilon,$$
| Utility of the Frisch-Waugh theorem
Consider the fixed effects panel data model, also known as Least Squares Dummy Variables (LSDV) model.
$b_{LSDV}$ can be computed by directly applying OLS to the model $$y=X\beta+D\alpha+\epsilon,$$
where $D$ is a $NT\times N$ matrix of dummies and $\alpha$ represent the individual-... | Utility of the Frisch-Waugh theorem
Consider the fixed effects panel data model, also known as Least Squares Dummy Variables (LSDV) model.
$b_{LSDV}$ can be computed by directly applying OLS to the model $$y=X\beta+D\alpha+\epsilon,$$
|
13,956 | Utility of the Frisch-Waugh theorem | Here is a simplified version of my first answer, which I believe is less practically relevant, but possibly easier to "sell" for classroom use.
The regressions $$y_i = \beta_1 + \sum_{j=2}^K\beta_jx_{ij} + \epsilon_i$$ and $$y_i-\bar{y} = \sum^K_{j=2}\beta_j(x_{ij} - \bar{x}_j) + \tilde{\epsilon}_i$$ yield identical $\... | Utility of the Frisch-Waugh theorem | Here is a simplified version of my first answer, which I believe is less practically relevant, but possibly easier to "sell" for classroom use.
The regressions $$y_i = \beta_1 + \sum_{j=2}^K\beta_jx_{ | Utility of the Frisch-Waugh theorem
Here is a simplified version of my first answer, which I believe is less practically relevant, but possibly easier to "sell" for classroom use.
The regressions $$y_i = \beta_1 + \sum_{j=2}^K\beta_jx_{ij} + \epsilon_i$$ and $$y_i-\bar{y} = \sum^K_{j=2}\beta_j(x_{ij} - \bar{x}_j) + \ti... | Utility of the Frisch-Waugh theorem
Here is a simplified version of my first answer, which I believe is less practically relevant, but possibly easier to "sell" for classroom use.
The regressions $$y_i = \beta_1 + \sum_{j=2}^K\beta_jx_{ |
13,957 | Utility of the Frisch-Waugh theorem | Here is another, more indirect, but I believe interesting one, namely the connection between different approaches to computing the partial autocorrelation coefficient of a stationary time series.
Definition 1
Consider the projection
\begin{equation}
\hat{Y}_{t}-\mu=\alpha^{(m)}_1(Y_{t-1}-\mu)+\alpha^{(m)}_2(Y_{t-2}-\mu... | Utility of the Frisch-Waugh theorem | Here is another, more indirect, but I believe interesting one, namely the connection between different approaches to computing the partial autocorrelation coefficient of a stationary time series.
Defi | Utility of the Frisch-Waugh theorem
Here is another, more indirect, but I believe interesting one, namely the connection between different approaches to computing the partial autocorrelation coefficient of a stationary time series.
Definition 1
Consider the projection
\begin{equation}
\hat{Y}_{t}-\mu=\alpha^{(m)}_1(Y_{... | Utility of the Frisch-Waugh theorem
Here is another, more indirect, but I believe interesting one, namely the connection between different approaches to computing the partial autocorrelation coefficient of a stationary time series.
Defi |
13,958 | Why scaling is important for the linear SVM classification? | SVM tries to maximize the distance between the separating plane and the support vectors. If one feature (i.e. one dimension in this space) has very large values, it will dominate the other features when calculating the distance. If you rescale all features (e.g. to [0, 1]), they all have the same influence on the dista... | Why scaling is important for the linear SVM classification? | SVM tries to maximize the distance between the separating plane and the support vectors. If one feature (i.e. one dimension in this space) has very large values, it will dominate the other features wh | Why scaling is important for the linear SVM classification?
SVM tries to maximize the distance between the separating plane and the support vectors. If one feature (i.e. one dimension in this space) has very large values, it will dominate the other features when calculating the distance. If you rescale all features (e.... | Why scaling is important for the linear SVM classification?
SVM tries to maximize the distance between the separating plane and the support vectors. If one feature (i.e. one dimension in this space) has very large values, it will dominate the other features wh |
13,959 | Why scaling is important for the linear SVM classification? | I think it can be made more clear through an example. Let's say you have two input vectors: X1 and X2. and let's say X1 has range(0.1 to 0.8) and X2 has range(3000 to 50000). Now your SVM classifier will be a linear boundary lying in X1-X2 plane. My claim is that the slope of linear decision boundary should not depend ... | Why scaling is important for the linear SVM classification? | I think it can be made more clear through an example. Let's say you have two input vectors: X1 and X2. and let's say X1 has range(0.1 to 0.8) and X2 has range(3000 to 50000). Now your SVM classifier w | Why scaling is important for the linear SVM classification?
I think it can be made more clear through an example. Let's say you have two input vectors: X1 and X2. and let's say X1 has range(0.1 to 0.8) and X2 has range(3000 to 50000). Now your SVM classifier will be a linear boundary lying in X1-X2 plane. My claim is t... | Why scaling is important for the linear SVM classification?
I think it can be made more clear through an example. Let's say you have two input vectors: X1 and X2. and let's say X1 has range(0.1 to 0.8) and X2 has range(3000 to 50000). Now your SVM classifier w |
13,960 | How do I interpret my regression with first differenced variables? | Suppose that we have the model
$$\begin{equation*} y_t = \beta_0 + \beta_1 x_t + \beta_2 t + \epsilon_t. \end{equation*}$$
You say that these coefficients are easier to interpret. Let's subtract $y_{t-1}$ from the lefthand side and $\beta_0 + \beta_1 x_{t-1} + \beta_2 ({t-1}) + \epsilon_{t-1}$, which equals $y_{t-1}$, ... | How do I interpret my regression with first differenced variables? | Suppose that we have the model
$$\begin{equation*} y_t = \beta_0 + \beta_1 x_t + \beta_2 t + \epsilon_t. \end{equation*}$$
You say that these coefficients are easier to interpret. Let's subtract $y_{t | How do I interpret my regression with first differenced variables?
Suppose that we have the model
$$\begin{equation*} y_t = \beta_0 + \beta_1 x_t + \beta_2 t + \epsilon_t. \end{equation*}$$
You say that these coefficients are easier to interpret. Let's subtract $y_{t-1}$ from the lefthand side and $\beta_0 + \beta_1 x_... | How do I interpret my regression with first differenced variables?
Suppose that we have the model
$$\begin{equation*} y_t = \beta_0 + \beta_1 x_t + \beta_2 t + \epsilon_t. \end{equation*}$$
You say that these coefficients are easier to interpret. Let's subtract $y_{t |
13,961 | How do I interpret my regression with first differenced variables? | First differencing removes linear trends that seem to persist in your original residuals. It looks like the first differencing removed the trend in the residuals and you are left with basically uncorrelated residuals. I am thinking that maybe the trend in the residuals hid part of the negative relationship between ER... | How do I interpret my regression with first differenced variables? | First differencing removes linear trends that seem to persist in your original residuals. It looks like the first differencing removed the trend in the residuals and you are left with basically uncor | How do I interpret my regression with first differenced variables?
First differencing removes linear trends that seem to persist in your original residuals. It looks like the first differencing removed the trend in the residuals and you are left with basically uncorrelated residuals. I am thinking that maybe the tren... | How do I interpret my regression with first differenced variables?
First differencing removes linear trends that seem to persist in your original residuals. It looks like the first differencing removed the trend in the residuals and you are left with basically uncor |
13,962 | Determining best fitting curve fitting function out of linear, exponential, and logarithmic functions | You might want to check out the free software called Eureqa. It has the specific aim of automating the process of finding both the functional form and the parameters of a given functional relationship.
If you are comparing models, with different numbers of parameters, you will generally want to use a measure of fit tha... | Determining best fitting curve fitting function out of linear, exponential, and logarithmic function | You might want to check out the free software called Eureqa. It has the specific aim of automating the process of finding both the functional form and the parameters of a given functional relationship | Determining best fitting curve fitting function out of linear, exponential, and logarithmic functions
You might want to check out the free software called Eureqa. It has the specific aim of automating the process of finding both the functional form and the parameters of a given functional relationship.
If you are compa... | Determining best fitting curve fitting function out of linear, exponential, and logarithmic function
You might want to check out the free software called Eureqa. It has the specific aim of automating the process of finding both the functional form and the parameters of a given functional relationship |
13,963 | Determining best fitting curve fitting function out of linear, exponential, and logarithmic functions | This is a question that is valid in very diverse domains.
The best model is the one that can predict data points that were not used during the parameter estimation. Ideally one would compute model parameters with a subset of the data set, and evaluate the fit performance on another data set. If you are interested in th... | Determining best fitting curve fitting function out of linear, exponential, and logarithmic function | This is a question that is valid in very diverse domains.
The best model is the one that can predict data points that were not used during the parameter estimation. Ideally one would compute model par | Determining best fitting curve fitting function out of linear, exponential, and logarithmic functions
This is a question that is valid in very diverse domains.
The best model is the one that can predict data points that were not used during the parameter estimation. Ideally one would compute model parameters with a sub... | Determining best fitting curve fitting function out of linear, exponential, and logarithmic function
This is a question that is valid in very diverse domains.
The best model is the one that can predict data points that were not used during the parameter estimation. Ideally one would compute model par |
13,964 | Determining best fitting curve fitting function out of linear, exponential, and logarithmic functions | Since plenty of people routinely explore the fit of various curves to their data, I don't know where your reservations are coming from. Granted, there is the fact that a quadratic will always fit at least as well as a linear, and a cubic, at least as well as a quadratic, so there are ways to test the statistical signi... | Determining best fitting curve fitting function out of linear, exponential, and logarithmic function | Since plenty of people routinely explore the fit of various curves to their data, I don't know where your reservations are coming from. Granted, there is the fact that a quadratic will always fit at | Determining best fitting curve fitting function out of linear, exponential, and logarithmic functions
Since plenty of people routinely explore the fit of various curves to their data, I don't know where your reservations are coming from. Granted, there is the fact that a quadratic will always fit at least as well as a... | Determining best fitting curve fitting function out of linear, exponential, and logarithmic function
Since plenty of people routinely explore the fit of various curves to their data, I don't know where your reservations are coming from. Granted, there is the fact that a quadratic will always fit at |
13,965 | Determining best fitting curve fitting function out of linear, exponential, and logarithmic functions | You really need to find a balance between the science/theory that leads to the data and what the data tells you. Like others have said, if you let yourself fit any possible transformation (polynomials of any degree, etc.) then you will end up overfitting and getting something that is useless.
One way to convince yours... | Determining best fitting curve fitting function out of linear, exponential, and logarithmic function | You really need to find a balance between the science/theory that leads to the data and what the data tells you. Like others have said, if you let yourself fit any possible transformation (polynomial | Determining best fitting curve fitting function out of linear, exponential, and logarithmic functions
You really need to find a balance between the science/theory that leads to the data and what the data tells you. Like others have said, if you let yourself fit any possible transformation (polynomials of any degree, e... | Determining best fitting curve fitting function out of linear, exponential, and logarithmic function
You really need to find a balance between the science/theory that leads to the data and what the data tells you. Like others have said, if you let yourself fit any possible transformation (polynomial |
13,966 | Can you say that statistics and probability is like induction and deduction? | I think it is the best to quickly recap the meaning of inductive and deductive reasoning before answering your question.
Deductive Reasoning: "Deductive arguments are attempts to show that a conclusion necessarily follows from a set of premises. A deductive argument is valid if the conclusion does follow necessarily f... | Can you say that statistics and probability is like induction and deduction? | I think it is the best to quickly recap the meaning of inductive and deductive reasoning before answering your question.
Deductive Reasoning: "Deductive arguments are attempts to show that a conclusi | Can you say that statistics and probability is like induction and deduction?
I think it is the best to quickly recap the meaning of inductive and deductive reasoning before answering your question.
Deductive Reasoning: "Deductive arguments are attempts to show that a conclusion necessarily follows from a set of premis... | Can you say that statistics and probability is like induction and deduction?
I think it is the best to quickly recap the meaning of inductive and deductive reasoning before answering your question.
Deductive Reasoning: "Deductive arguments are attempts to show that a conclusi |
13,967 | Can you say that statistics and probability is like induction and deduction? | Statistics is the deductive approach to induction. Consider the two main approaches to statistical inference: Frequentist and Bayesian.
Assume you are a Frequentist (in the style of Fisher, rather than Neyman for convenience). You wonder whether a parameter of substantive interest takes a particular value, so you c... | Can you say that statistics and probability is like induction and deduction? | Statistics is the deductive approach to induction. Consider the two main approaches to statistical inference: Frequentist and Bayesian.
Assume you are a Frequentist (in the style of Fisher, rather | Can you say that statistics and probability is like induction and deduction?
Statistics is the deductive approach to induction. Consider the two main approaches to statistical inference: Frequentist and Bayesian.
Assume you are a Frequentist (in the style of Fisher, rather than Neyman for convenience). You wonder w... | Can you say that statistics and probability is like induction and deduction?
Statistics is the deductive approach to induction. Consider the two main approaches to statistical inference: Frequentist and Bayesian.
Assume you are a Frequentist (in the style of Fisher, rather |
13,968 | Can you say that statistics and probability is like induction and deduction? | Yes! Maybe statistics isn't strictly equal to induction, but statistics is the solution to the problem of induction in my opinion. | Can you say that statistics and probability is like induction and deduction? | Yes! Maybe statistics isn't strictly equal to induction, but statistics is the solution to the problem of induction in my opinion. | Can you say that statistics and probability is like induction and deduction?
Yes! Maybe statistics isn't strictly equal to induction, but statistics is the solution to the problem of induction in my opinion. | Can you say that statistics and probability is like induction and deduction?
Yes! Maybe statistics isn't strictly equal to induction, but statistics is the solution to the problem of induction in my opinion. |
13,969 | Can you say that statistics and probability is like induction and deduction? | Induction: for a statistical problem, the sample along with inferential statistics allows us to draw conclusions about the population, with inferential statistics making clear use of elements of probability.
Deduction: elements in probability allow us to draw conclusions about the characteristics of hypothetical data t... | Can you say that statistics and probability is like induction and deduction? | Induction: for a statistical problem, the sample along with inferential statistics allows us to draw conclusions about the population, with inferential statistics making clear use of elements of proba | Can you say that statistics and probability is like induction and deduction?
Induction: for a statistical problem, the sample along with inferential statistics allows us to draw conclusions about the population, with inferential statistics making clear use of elements of probability.
Deduction: elements in probability ... | Can you say that statistics and probability is like induction and deduction?
Induction: for a statistical problem, the sample along with inferential statistics allows us to draw conclusions about the population, with inferential statistics making clear use of elements of proba |
13,970 | What are some good blogs for Mathematical Statistics and Machine Learning? | Francis Bach's Machine Learning Research blog is an "easy to digest" introduction to some of his research works and related topics ("easy" as in easier than reading the original papers).
It contains many excellent in-depth writings about kernel methods, optimization algorithms, linear algebra and highlights how these t... | What are some good blogs for Mathematical Statistics and Machine Learning? | Francis Bach's Machine Learning Research blog is an "easy to digest" introduction to some of his research works and related topics ("easy" as in easier than reading the original papers).
It contains m | What are some good blogs for Mathematical Statistics and Machine Learning?
Francis Bach's Machine Learning Research blog is an "easy to digest" introduction to some of his research works and related topics ("easy" as in easier than reading the original papers).
It contains many excellent in-depth writings about kernel ... | What are some good blogs for Mathematical Statistics and Machine Learning?
Francis Bach's Machine Learning Research blog is an "easy to digest" introduction to some of his research works and related topics ("easy" as in easier than reading the original papers).
It contains m |
13,971 | What are some good blogs for Mathematical Statistics and Machine Learning? | Andrew Gelman: https://statmodeling.stat.columbia.edu. Gelman is a professor of statistics and political science at Columbia, and has co-authored several statistics books, including Bayesian Data Analysis and Regression and Other Stories. I strongly disagree with most of his politics, but his statistics is generally so... | What are some good blogs for Mathematical Statistics and Machine Learning? | Andrew Gelman: https://statmodeling.stat.columbia.edu. Gelman is a professor of statistics and political science at Columbia, and has co-authored several statistics books, including Bayesian Data Anal | What are some good blogs for Mathematical Statistics and Machine Learning?
Andrew Gelman: https://statmodeling.stat.columbia.edu. Gelman is a professor of statistics and political science at Columbia, and has co-authored several statistics books, including Bayesian Data Analysis and Regression and Other Stories. I stro... | What are some good blogs for Mathematical Statistics and Machine Learning?
Andrew Gelman: https://statmodeling.stat.columbia.edu. Gelman is a professor of statistics and political science at Columbia, and has co-authored several statistics books, including Bayesian Data Anal |
13,972 | What are some good blogs for Mathematical Statistics and Machine Learning? | https://statisticaloddsandends.wordpress.com/ reminds me of Gunderson blog, nicely written with code and clear explanations. | What are some good blogs for Mathematical Statistics and Machine Learning? | https://statisticaloddsandends.wordpress.com/ reminds me of Gunderson blog, nicely written with code and clear explanations. | What are some good blogs for Mathematical Statistics and Machine Learning?
https://statisticaloddsandends.wordpress.com/ reminds me of Gunderson blog, nicely written with code and clear explanations. | What are some good blogs for Mathematical Statistics and Machine Learning?
https://statisticaloddsandends.wordpress.com/ reminds me of Gunderson blog, nicely written with code and clear explanations. |
13,973 | What are some good blogs for Mathematical Statistics and Machine Learning? | ICLR recently introduced its Blog Track and its taken inspiration from some blogs like Bach's. Best thing is that it's peer-reviewed and contains diverse topics from diverse authors (often a group of authors). | What are some good blogs for Mathematical Statistics and Machine Learning? | ICLR recently introduced its Blog Track and its taken inspiration from some blogs like Bach's. Best thing is that it's peer-reviewed and contains diverse topics from diverse authors (often a group of | What are some good blogs for Mathematical Statistics and Machine Learning?
ICLR recently introduced its Blog Track and its taken inspiration from some blogs like Bach's. Best thing is that it's peer-reviewed and contains diverse topics from diverse authors (often a group of authors). | What are some good blogs for Mathematical Statistics and Machine Learning?
ICLR recently introduced its Blog Track and its taken inspiration from some blogs like Bach's. Best thing is that it's peer-reviewed and contains diverse topics from diverse authors (often a group of |
13,974 | What are some good blogs for Mathematical Statistics and Machine Learning? | In the last couple of years I have warmed up to using geometry to understand deep learning models, and indeed various types of statistical models. While I recommend the book Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges, you can also find a list of blogs related to the topic. | What are some good blogs for Mathematical Statistics and Machine Learning? | In the last couple of years I have warmed up to using geometry to understand deep learning models, and indeed various types of statistical models. While I recommend the book Geometric Deep Learning: G | What are some good blogs for Mathematical Statistics and Machine Learning?
In the last couple of years I have warmed up to using geometry to understand deep learning models, and indeed various types of statistical models. While I recommend the book Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges, ... | What are some good blogs for Mathematical Statistics and Machine Learning?
In the last couple of years I have warmed up to using geometry to understand deep learning models, and indeed various types of statistical models. While I recommend the book Geometric Deep Learning: G |
13,975 | What are some good blogs for Mathematical Statistics and Machine Learning? | An Outsider's Tour of Reinforcement Learning by Ben Recht gives a short introduction into RL and draws connection to control theory. | What are some good blogs for Mathematical Statistics and Machine Learning? | An Outsider's Tour of Reinforcement Learning by Ben Recht gives a short introduction into RL and draws connection to control theory. | What are some good blogs for Mathematical Statistics and Machine Learning?
An Outsider's Tour of Reinforcement Learning by Ben Recht gives a short introduction into RL and draws connection to control theory. | What are some good blogs for Mathematical Statistics and Machine Learning?
An Outsider's Tour of Reinforcement Learning by Ben Recht gives a short introduction into RL and draws connection to control theory. |
13,976 | What are some good blogs for Mathematical Statistics and Machine Learning? | This is neither really a blog nor just about statistics and many times very basic, but I found many good advices and ideas in there so I decided to add it as an answer
https://chrisalbon.com/#code_statistics | What are some good blogs for Mathematical Statistics and Machine Learning? | This is neither really a blog nor just about statistics and many times very basic, but I found many good advices and ideas in there so I decided to add it as an answer
https://chrisalbon.com/#code_sta | What are some good blogs for Mathematical Statistics and Machine Learning?
This is neither really a blog nor just about statistics and many times very basic, but I found many good advices and ideas in there so I decided to add it as an answer
https://chrisalbon.com/#code_statistics | What are some good blogs for Mathematical Statistics and Machine Learning?
This is neither really a blog nor just about statistics and many times very basic, but I found many good advices and ideas in there so I decided to add it as an answer
https://chrisalbon.com/#code_sta |
13,977 | What are some good blogs for Mathematical Statistics and Machine Learning? | Towards data science a collection of articles focussing on data science, machine learning, artificial intelligence and programming. It is written by various authors. The articles often focus on explaining some technique or area.
A quick search finds some links on the website here but possibly there are more indirect l... | What are some good blogs for Mathematical Statistics and Machine Learning? | Towards data science a collection of articles focussing on data science, machine learning, artificial intelligence and programming. It is written by various authors. The articles often focus on explai | What are some good blogs for Mathematical Statistics and Machine Learning?
Towards data science a collection of articles focussing on data science, machine learning, artificial intelligence and programming. It is written by various authors. The articles often focus on explaining some technique or area.
A quick search f... | What are some good blogs for Mathematical Statistics and Machine Learning?
Towards data science a collection of articles focussing on data science, machine learning, artificial intelligence and programming. It is written by various authors. The articles often focus on explai |
13,978 | How do sample weights work in classification models? | As Frans Rodenburg already correctly stated in his comment, in most cases instance or sample weights factor into the loss function that is being optimized by the method in question.
Consider the equation the documentation provides for the primal problem of the C-SVM
$$\min_{w,b,\zeta} \frac{1}{2}w^Tw + C\sum_{i=1}^{n}... | How do sample weights work in classification models? | As Frans Rodenburg already correctly stated in his comment, in most cases instance or sample weights factor into the loss function that is being optimized by the method in question.
Consider the equa | How do sample weights work in classification models?
As Frans Rodenburg already correctly stated in his comment, in most cases instance or sample weights factor into the loss function that is being optimized by the method in question.
Consider the equation the documentation provides for the primal problem of the C-SVM... | How do sample weights work in classification models?
As Frans Rodenburg already correctly stated in his comment, in most cases instance or sample weights factor into the loss function that is being optimized by the method in question.
Consider the equa |
13,979 | How do sample weights work in classification models? | Rickyfox's answer is great in explaining how the weights influence the results of a classifier, but maybe could you be also interested in why / how we would need such weights in the first place (which is more a statistical problem than a purely ML one).
Sometimes the observed data is observed with different distributio... | How do sample weights work in classification models? | Rickyfox's answer is great in explaining how the weights influence the results of a classifier, but maybe could you be also interested in why / how we would need such weights in the first place (which | How do sample weights work in classification models?
Rickyfox's answer is great in explaining how the weights influence the results of a classifier, but maybe could you be also interested in why / how we would need such weights in the first place (which is more a statistical problem than a purely ML one).
Sometimes the... | How do sample weights work in classification models?
Rickyfox's answer is great in explaining how the weights influence the results of a classifier, but maybe could you be also interested in why / how we would need such weights in the first place (which |
13,980 | Why do RNNs have a tendency to suffer from vanishing/exploding gradient? | TL;DR
The main reasons are the following traits of BPTT:
An unrolled RNN tends to be a very deep network.
In an unrolled RNN the gradient in an early layer is a product that (also) contains many instances of the same term.
Long Version
To train an RNN, people usually use backpropagation through time (BPTT), which mea... | Why do RNNs have a tendency to suffer from vanishing/exploding gradient? | TL;DR
The main reasons are the following traits of BPTT:
An unrolled RNN tends to be a very deep network.
In an unrolled RNN the gradient in an early layer is a product that (also) contains many inst | Why do RNNs have a tendency to suffer from vanishing/exploding gradient?
TL;DR
The main reasons are the following traits of BPTT:
An unrolled RNN tends to be a very deep network.
In an unrolled RNN the gradient in an early layer is a product that (also) contains many instances of the same term.
Long Version
To train ... | Why do RNNs have a tendency to suffer from vanishing/exploding gradient?
TL;DR
The main reasons are the following traits of BPTT:
An unrolled RNN tends to be a very deep network.
In an unrolled RNN the gradient in an early layer is a product that (also) contains many inst |
13,981 | Why do RNNs have a tendency to suffer from vanishing/exploding gradient? | Because RNN is trained by backpropagation through time, and therefore unfolded into feed forward net with multiple layers. When gradient is passed back through many time steps, it tends to grow or vanish, same way as it happens in deep feedforward nets | Why do RNNs have a tendency to suffer from vanishing/exploding gradient? | Because RNN is trained by backpropagation through time, and therefore unfolded into feed forward net with multiple layers. When gradient is passed back through many time steps, it tends to grow or van | Why do RNNs have a tendency to suffer from vanishing/exploding gradient?
Because RNN is trained by backpropagation through time, and therefore unfolded into feed forward net with multiple layers. When gradient is passed back through many time steps, it tends to grow or vanish, same way as it happens in deep feedforward... | Why do RNNs have a tendency to suffer from vanishing/exploding gradient?
Because RNN is trained by backpropagation through time, and therefore unfolded into feed forward net with multiple layers. When gradient is passed back through many time steps, it tends to grow or van |
13,982 | Why do RNNs have a tendency to suffer from vanishing/exploding gradient? | I would like to point out one point that the answers above seems to have missed about vanishing gradient in RNN.
What people mean by vanishing gradient should be understood differently from the original meaning in DNN. But first we need to make some notation.
Let $h_0 \neq 0$, the recursive formula for Elman Recurrent ... | Why do RNNs have a tendency to suffer from vanishing/exploding gradient? | I would like to point out one point that the answers above seems to have missed about vanishing gradient in RNN.
What people mean by vanishing gradient should be understood differently from the origin | Why do RNNs have a tendency to suffer from vanishing/exploding gradient?
I would like to point out one point that the answers above seems to have missed about vanishing gradient in RNN.
What people mean by vanishing gradient should be understood differently from the original meaning in DNN. But first we need to make so... | Why do RNNs have a tendency to suffer from vanishing/exploding gradient?
I would like to point out one point that the answers above seems to have missed about vanishing gradient in RNN.
What people mean by vanishing gradient should be understood differently from the origin |
13,983 | Why do RNNs have a tendency to suffer from vanishing/exploding gradient? | This chapter describes the reason for vanishing gradient problem really well. When we unfold the RNN over time it is also like a deep neural network. Therefore according to my understanding it also suffers from vanishing gradient problem as deep feedforward nets. | Why do RNNs have a tendency to suffer from vanishing/exploding gradient? | This chapter describes the reason for vanishing gradient problem really well. When we unfold the RNN over time it is also like a deep neural network. Therefore according to my understanding it also su | Why do RNNs have a tendency to suffer from vanishing/exploding gradient?
This chapter describes the reason for vanishing gradient problem really well. When we unfold the RNN over time it is also like a deep neural network. Therefore according to my understanding it also suffers from vanishing gradient problem as deep f... | Why do RNNs have a tendency to suffer from vanishing/exploding gradient?
This chapter describes the reason for vanishing gradient problem really well. When we unfold the RNN over time it is also like a deep neural network. Therefore according to my understanding it also su |
13,984 | Low variance components in PCA, are they really just noise? Is there any way to test for it? | One way of testing the randomness of a small principal component (PC) is to treat it like a signal instead of noise: i.e., try to predict another variable of interest with it. This is essentially principal components regression (PCR).
In the predictive context of PCR, Lott (1973) recommends selecting PCs in a way that... | Low variance components in PCA, are they really just noise? Is there any way to test for it? | One way of testing the randomness of a small principal component (PC) is to treat it like a signal instead of noise: i.e., try to predict another variable of interest with it. This is essentially prin | Low variance components in PCA, are they really just noise? Is there any way to test for it?
One way of testing the randomness of a small principal component (PC) is to treat it like a signal instead of noise: i.e., try to predict another variable of interest with it. This is essentially principal components regression... | Low variance components in PCA, are they really just noise? Is there any way to test for it?
One way of testing the randomness of a small principal component (PC) is to treat it like a signal instead of noise: i.e., try to predict another variable of interest with it. This is essentially prin |
13,985 | Low variance components in PCA, are they really just noise? Is there any way to test for it? | Adding to @Nick Stauner's answer, when you're dealing with subspace clustering, PCA is often a poor solution.
When using PCA, one is mostly concerned about the eigenvectors with the highest eigenvalues, which represent the directions towards which the data is 'stretched' the most. If your data is comprised of small sub... | Low variance components in PCA, are they really just noise? Is there any way to test for it? | Adding to @Nick Stauner's answer, when you're dealing with subspace clustering, PCA is often a poor solution.
When using PCA, one is mostly concerned about the eigenvectors with the highest eigenvalue | Low variance components in PCA, are they really just noise? Is there any way to test for it?
Adding to @Nick Stauner's answer, when you're dealing with subspace clustering, PCA is often a poor solution.
When using PCA, one is mostly concerned about the eigenvectors with the highest eigenvalues, which represent the dire... | Low variance components in PCA, are they really just noise? Is there any way to test for it?
Adding to @Nick Stauner's answer, when you're dealing with subspace clustering, PCA is often a poor solution.
When using PCA, one is mostly concerned about the eigenvectors with the highest eigenvalue |
13,986 | Calculate variance explained by each predictor in multiple regression using R | The percentage explained depends on the order entered.
If you specify a particular order, you can compute this trivially in R (e.g. via the update and anova functions, see below), but a different order of entry would yield potentially very different answers.
[One possibility might be to average across all orders or s... | Calculate variance explained by each predictor in multiple regression using R | The percentage explained depends on the order entered.
If you specify a particular order, you can compute this trivially in R (e.g. via the update and anova functions, see below), but a different ord | Calculate variance explained by each predictor in multiple regression using R
The percentage explained depends on the order entered.
If you specify a particular order, you can compute this trivially in R (e.g. via the update and anova functions, see below), but a different order of entry would yield potentially very d... | Calculate variance explained by each predictor in multiple regression using R
The percentage explained depends on the order entered.
If you specify a particular order, you can compute this trivially in R (e.g. via the update and anova functions, see below), but a different ord |
13,987 | Calculate variance explained by each predictor in multiple regression using R | I proved that the percentage of variation explained by a given predictor in a multiple linear regression is the product of the slope coefficient and the correlation of the predictor with the fitted values of the dependent variable (assuming that all variables have been standardized to have mean zero and variance one; w... | Calculate variance explained by each predictor in multiple regression using R | I proved that the percentage of variation explained by a given predictor in a multiple linear regression is the product of the slope coefficient and the correlation of the predictor with the fitted va | Calculate variance explained by each predictor in multiple regression using R
I proved that the percentage of variation explained by a given predictor in a multiple linear regression is the product of the slope coefficient and the correlation of the predictor with the fitted values of the dependent variable (assuming t... | Calculate variance explained by each predictor in multiple regression using R
I proved that the percentage of variation explained by a given predictor in a multiple linear regression is the product of the slope coefficient and the correlation of the predictor with the fitted va |
13,988 | Calculate variance explained by each predictor in multiple regression using R | You can use hier.part library to have goodness of fit measures for regressions of a single dependent variable to all combinations of N independent variables
library(hier.part)
env <- D[,2:5]
all.regs(D$dv, env, fam = "gaussian", gof = "Rsqu",
print.vars = TRUE) | Calculate variance explained by each predictor in multiple regression using R | You can use hier.part library to have goodness of fit measures for regressions of a single dependent variable to all combinations of N independent variables
library(hier.part)
env <- D[,2:5]
all.regs( | Calculate variance explained by each predictor in multiple regression using R
You can use hier.part library to have goodness of fit measures for regressions of a single dependent variable to all combinations of N independent variables
library(hier.part)
env <- D[,2:5]
all.regs(D$dv, env, fam = "gaussian", gof = "Rsqu",... | Calculate variance explained by each predictor in multiple regression using R
You can use hier.part library to have goodness of fit measures for regressions of a single dependent variable to all combinations of N independent variables
library(hier.part)
env <- D[,2:5]
all.regs( |
13,989 | Calculate variance explained by each predictor in multiple regression using R | I am just re-posting the comment of @Phil here because this is clearly the best answer:
I would suggest looking into the relaimpo package, and its accompanying paper: jstatsoft.org/index.php/jss/article/view/v017i01/v17i01.pdf I use the "LMG" method frequently.
I have been searching the answer to this question for 5 ... | Calculate variance explained by each predictor in multiple regression using R | I am just re-posting the comment of @Phil here because this is clearly the best answer:
I would suggest looking into the relaimpo package, and its accompanying paper: jstatsoft.org/index.php/jss/arti | Calculate variance explained by each predictor in multiple regression using R
I am just re-posting the comment of @Phil here because this is clearly the best answer:
I would suggest looking into the relaimpo package, and its accompanying paper: jstatsoft.org/index.php/jss/article/view/v017i01/v17i01.pdf I use the "LMG... | Calculate variance explained by each predictor in multiple regression using R
I am just re-posting the comment of @Phil here because this is clearly the best answer:
I would suggest looking into the relaimpo package, and its accompanying paper: jstatsoft.org/index.php/jss/arti |
13,990 | How to visualize a 3-D density function? | Well there are four possible approaches that come to mind (although I am sure that there are many more) but basically you could either plot the data as a perspective plot, a contour plot, a heat map or if you prefer a 3-D scatter plot (which is more or less a perspective plot when you have values of $z$ for all $(x,y)$... | How to visualize a 3-D density function? | Well there are four possible approaches that come to mind (although I am sure that there are many more) but basically you could either plot the data as a perspective plot, a contour plot, a heat map o | How to visualize a 3-D density function?
Well there are four possible approaches that come to mind (although I am sure that there are many more) but basically you could either plot the data as a perspective plot, a contour plot, a heat map or if you prefer a 3-D scatter plot (which is more or less a perspective plot wh... | How to visualize a 3-D density function?
Well there are four possible approaches that come to mind (although I am sure that there are many more) but basically you could either plot the data as a perspective plot, a contour plot, a heat map o |
13,991 | Is there "unsupervised regression"? | I've never encountered this term before. I am unsure whether it would spread light or darkness within either realm of statistics: those being machine learning (where supervised and unsupervised distinctions are central to problem solving) and inferential statistics (where regression, confirmatory analysis, and NHSTs ar... | Is there "unsupervised regression"? | I've never encountered this term before. I am unsure whether it would spread light or darkness within either realm of statistics: those being machine learning (where supervised and unsupervised distin | Is there "unsupervised regression"?
I've never encountered this term before. I am unsure whether it would spread light or darkness within either realm of statistics: those being machine learning (where supervised and unsupervised distinctions are central to problem solving) and inferential statistics (where regression,... | Is there "unsupervised regression"?
I've never encountered this term before. I am unsure whether it would spread light or darkness within either realm of statistics: those being machine learning (where supervised and unsupervised distin |
13,992 | Is there "unsupervised regression"? | The closest thing I can think of is a little black magic that stirred people up when it was announced a few years ago, but I don't believe it gained any real traction in the community. The authors developed a statistic they called the "Maximal Information Coefficient (MIC)." The general idea behind their method is to t... | Is there "unsupervised regression"? | The closest thing I can think of is a little black magic that stirred people up when it was announced a few years ago, but I don't believe it gained any real traction in the community. The authors dev | Is there "unsupervised regression"?
The closest thing I can think of is a little black magic that stirred people up when it was announced a few years ago, but I don't believe it gained any real traction in the community. The authors developed a statistic they called the "Maximal Information Coefficient (MIC)." The gene... | Is there "unsupervised regression"?
The closest thing I can think of is a little black magic that stirred people up when it was announced a few years ago, but I don't believe it gained any real traction in the community. The authors dev |
13,993 | Is there "unsupervised regression"? | Auto regression is one way to compute weights of a matrix minimizing error on reconstructed input from given input. | Is there "unsupervised regression"? | Auto regression is one way to compute weights of a matrix minimizing error on reconstructed input from given input. | Is there "unsupervised regression"?
Auto regression is one way to compute weights of a matrix minimizing error on reconstructed input from given input. | Is there "unsupervised regression"?
Auto regression is one way to compute weights of a matrix minimizing error on reconstructed input from given input. |
13,994 | Is there "unsupervised regression"? | This question came to my mind while researching the difference between supervised and unsupervised methods. Coming from an econometric background I prefer to think in models, which slowed my understanding as most machine learning literature I encountered focuses on methods.
What I have found thus far is that a strict d... | Is there "unsupervised regression"? | This question came to my mind while researching the difference between supervised and unsupervised methods. Coming from an econometric background I prefer to think in models, which slowed my understan | Is there "unsupervised regression"?
This question came to my mind while researching the difference between supervised and unsupervised methods. Coming from an econometric background I prefer to think in models, which slowed my understanding as most machine learning literature I encountered focuses on methods.
What I ha... | Is there "unsupervised regression"?
This question came to my mind while researching the difference between supervised and unsupervised methods. Coming from an econometric background I prefer to think in models, which slowed my understan |
13,995 | Is there a formula or rule for determining the correct sampSize for a randomForest? | In general, the sample size for a random forest acts as a control on the "degree of randomness" involved, and thus as a way of adjusting the bias-variance tradeoff. Increasing the sample size results in a "less random" forest, and so has a tendency to overfit. Decreasing the sample size increases the variation in the i... | Is there a formula or rule for determining the correct sampSize for a randomForest? | In general, the sample size for a random forest acts as a control on the "degree of randomness" involved, and thus as a way of adjusting the bias-variance tradeoff. Increasing the sample size results | Is there a formula or rule for determining the correct sampSize for a randomForest?
In general, the sample size for a random forest acts as a control on the "degree of randomness" involved, and thus as a way of adjusting the bias-variance tradeoff. Increasing the sample size results in a "less random" forest, and so ha... | Is there a formula or rule for determining the correct sampSize for a randomForest?
In general, the sample size for a random forest acts as a control on the "degree of randomness" involved, and thus as a way of adjusting the bias-variance tradeoff. Increasing the sample size results |
13,996 | Is there a formula or rule for determining the correct sampSize for a randomForest? | For random forests to work as well in new data as they do in training data, the required sample size is enormous, often being 200 times the number of candidate features. See here. | Is there a formula or rule for determining the correct sampSize for a randomForest? | For random forests to work as well in new data as they do in training data, the required sample size is enormous, often being 200 times the number of candidate features. See here. | Is there a formula or rule for determining the correct sampSize for a randomForest?
For random forests to work as well in new data as they do in training data, the required sample size is enormous, often being 200 times the number of candidate features. See here. | Is there a formula or rule for determining the correct sampSize for a randomForest?
For random forests to work as well in new data as they do in training data, the required sample size is enormous, often being 200 times the number of candidate features. See here. |
13,997 | Is there a formula or rule for determining the correct sampSize for a randomForest? | I ran 4500 random forests over night with some random parameter-settings:
Regression problem Ysignal = x1^2+sin(x2*pi) + x3 * x4 + x5
where any x are sampled independent from a normal distribution, sd=1, mean=1
Ytotal = Ysignal + Yerror
where Yerror = rnorm(n.observations,sd=sd(Ysignal))*noise.factor
theoretical.expla... | Is there a formula or rule for determining the correct sampSize for a randomForest? | I ran 4500 random forests over night with some random parameter-settings:
Regression problem Ysignal = x1^2+sin(x2*pi) + x3 * x4 + x5
where any x are sampled independent from a normal distribution, sd | Is there a formula or rule for determining the correct sampSize for a randomForest?
I ran 4500 random forests over night with some random parameter-settings:
Regression problem Ysignal = x1^2+sin(x2*pi) + x3 * x4 + x5
where any x are sampled independent from a normal distribution, sd=1, mean=1
Ytotal = Ysignal + Yerror... | Is there a formula or rule for determining the correct sampSize for a randomForest?
I ran 4500 random forests over night with some random parameter-settings:
Regression problem Ysignal = x1^2+sin(x2*pi) + x3 * x4 + x5
where any x are sampled independent from a normal distribution, sd |
13,998 | Ideas for "lab notebook" software? | These are called Electronic Lab Notebooks (ELN).
Here are some of the open source options I've looked at:
The Sage Notebook.
The new IPython Notebook, which can now be run as a webapp on EC2 and Azure.
Leo, which can be used with IPython and in many other ways.
Various wiki, blogging, and CMS solutions. | Ideas for "lab notebook" software? | These are called Electronic Lab Notebooks (ELN).
Here are some of the open source options I've looked at:
The Sage Notebook.
The new IPython Notebook, which can now be run as a webapp on EC2 and Azur | Ideas for "lab notebook" software?
These are called Electronic Lab Notebooks (ELN).
Here are some of the open source options I've looked at:
The Sage Notebook.
The new IPython Notebook, which can now be run as a webapp on EC2 and Azure.
Leo, which can be used with IPython and in many other ways.
Various wiki, blogging... | Ideas for "lab notebook" software?
These are called Electronic Lab Notebooks (ELN).
Here are some of the open source options I've looked at:
The Sage Notebook.
The new IPython Notebook, which can now be run as a webapp on EC2 and Azur |
13,999 | Ideas for "lab notebook" software? | My favorite: Evernote. You can tag entries (e.g., 'analysis', 'idea', etc.), you can paste pictures and graphics, and you can share notebooks with collaborators. And: it's basically free (well, freemium). But the free edition is absolutely sufficient for me. | Ideas for "lab notebook" software? | My favorite: Evernote. You can tag entries (e.g., 'analysis', 'idea', etc.), you can paste pictures and graphics, and you can share notebooks with collaborators. And: it's basically free (well, freemi | Ideas for "lab notebook" software?
My favorite: Evernote. You can tag entries (e.g., 'analysis', 'idea', etc.), you can paste pictures and graphics, and you can share notebooks with collaborators. And: it's basically free (well, freemium). But the free edition is absolutely sufficient for me. | Ideas for "lab notebook" software?
My favorite: Evernote. You can tag entries (e.g., 'analysis', 'idea', etc.), you can paste pictures and graphics, and you can share notebooks with collaborators. And: it's basically free (well, freemi |
14,000 | Ideas for "lab notebook" software? | I've never used it personally, but Microsoft has a piece of software in the Office suite called OneNote that accomplishes a similar goal to your e-lab notebook specifications. Refer to their website for more information. They also offer a free trial bundled with MS Office here. | Ideas for "lab notebook" software? | I've never used it personally, but Microsoft has a piece of software in the Office suite called OneNote that accomplishes a similar goal to your e-lab notebook specifications. Refer to their website f | Ideas for "lab notebook" software?
I've never used it personally, but Microsoft has a piece of software in the Office suite called OneNote that accomplishes a similar goal to your e-lab notebook specifications. Refer to their website for more information. They also offer a free trial bundled with MS Office here. | Ideas for "lab notebook" software?
I've never used it personally, but Microsoft has a piece of software in the Office suite called OneNote that accomplishes a similar goal to your e-lab notebook specifications. Refer to their website f |
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