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 |
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50,401 | predictive distribution of bayesian logistic regression | (1) involves a multivariate Gaussian with potentially off-diagonal covariance matrix terms. You can see this from the previous section of Bishop, which gives the form of $q(w)$ as
$$
q(w) = \mathcal{N}(w \mid w_{\text{MAP}}, S_N),
$$
where $S_N$ can generally have off-diagonal terms. This is replaced by an integral ov... | predictive distribution of bayesian logistic regression | (1) involves a multivariate Gaussian with potentially off-diagonal covariance matrix terms. You can see this from the previous section of Bishop, which gives the form of $q(w)$ as
$$
q(w) = \mathcal{N | predictive distribution of bayesian logistic regression
(1) involves a multivariate Gaussian with potentially off-diagonal covariance matrix terms. You can see this from the previous section of Bishop, which gives the form of $q(w)$ as
$$
q(w) = \mathcal{N}(w \mid w_{\text{MAP}}, S_N),
$$
where $S_N$ can generally hav... | predictive distribution of bayesian logistic regression
(1) involves a multivariate Gaussian with potentially off-diagonal covariance matrix terms. You can see this from the previous section of Bishop, which gives the form of $q(w)$ as
$$
q(w) = \mathcal{N |
50,402 | Evaluation of log Vs. non log models | Yes, what you describe is a logical approach.
Aside (back-)transforming the response variable I would suggest considering a model that does not rely heavily on assumptions regarding the model's error-structure and/or the distribution of the response variable. Immediate regression-like alternatives would be robust regre... | Evaluation of log Vs. non log models | Yes, what you describe is a logical approach.
Aside (back-)transforming the response variable I would suggest considering a model that does not rely heavily on assumptions regarding the model's error- | Evaluation of log Vs. non log models
Yes, what you describe is a logical approach.
Aside (back-)transforming the response variable I would suggest considering a model that does not rely heavily on assumptions regarding the model's error-structure and/or the distribution of the response variable. Immediate regression-li... | Evaluation of log Vs. non log models
Yes, what you describe is a logical approach.
Aside (back-)transforming the response variable I would suggest considering a model that does not rely heavily on assumptions regarding the model's error- |
50,403 | How to compute a weighted AUC? | Not sure if this question is still valid, but you can use PRROC package in R for weighted AUC computations. | How to compute a weighted AUC? | Not sure if this question is still valid, but you can use PRROC package in R for weighted AUC computations. | How to compute a weighted AUC?
Not sure if this question is still valid, but you can use PRROC package in R for weighted AUC computations. | How to compute a weighted AUC?
Not sure if this question is still valid, but you can use PRROC package in R for weighted AUC computations. |
50,404 | Plotting (multilevel) multiple regression [closed] | There are a number of packages that support the plotting of marginal effects of fixed effects in a mixed model. I'm aware of the following: visreg, effects, ggeffects and sjPlot. In what follows below, I illustrate the usage of visreg and ggeffects.
Using visreg
The visreg package supports plotting fixed effects as wel... | Plotting (multilevel) multiple regression [closed] | There are a number of packages that support the plotting of marginal effects of fixed effects in a mixed model. I'm aware of the following: visreg, effects, ggeffects and sjPlot. In what follows below | Plotting (multilevel) multiple regression [closed]
There are a number of packages that support the plotting of marginal effects of fixed effects in a mixed model. I'm aware of the following: visreg, effects, ggeffects and sjPlot. In what follows below, I illustrate the usage of visreg and ggeffects.
Using visreg
The vi... | Plotting (multilevel) multiple regression [closed]
There are a number of packages that support the plotting of marginal effects of fixed effects in a mixed model. I'm aware of the following: visreg, effects, ggeffects and sjPlot. In what follows below |
50,405 | Why does locally connected layer work in convolutional neural network? | In a convolution layer the filter has an output depth parameter. So a 5x5 filter is actually 5x5xd, d being the output depth. This essentially means that there are 'd' different filters, each of which will (learn to) have different weights. Each filter will detect the presence of a particular pattern across a feature m... | Why does locally connected layer work in convolutional neural network? | In a convolution layer the filter has an output depth parameter. So a 5x5 filter is actually 5x5xd, d being the output depth. This essentially means that there are 'd' different filters, each of which | Why does locally connected layer work in convolutional neural network?
In a convolution layer the filter has an output depth parameter. So a 5x5 filter is actually 5x5xd, d being the output depth. This essentially means that there are 'd' different filters, each of which will (learn to) have different weights. Each fil... | Why does locally connected layer work in convolutional neural network?
In a convolution layer the filter has an output depth parameter. So a 5x5 filter is actually 5x5xd, d being the output depth. This essentially means that there are 'd' different filters, each of which |
50,406 | Why does locally connected layer work in convolutional neural network? | This is because if a filter is successful in extracting a useful feature from a small portion of the image, we would like to extract the same feature from other parts of the image. This is related to this fact that we would like to extract translation-invariant features from the image, that is we want to extract featur... | Why does locally connected layer work in convolutional neural network? | This is because if a filter is successful in extracting a useful feature from a small portion of the image, we would like to extract the same feature from other parts of the image. This is related to | Why does locally connected layer work in convolutional neural network?
This is because if a filter is successful in extracting a useful feature from a small portion of the image, we would like to extract the same feature from other parts of the image. This is related to this fact that we would like to extract translati... | Why does locally connected layer work in convolutional neural network?
This is because if a filter is successful in extracting a useful feature from a small portion of the image, we would like to extract the same feature from other parts of the image. This is related to |
50,407 | Why does locally connected layer work in convolutional neural network? | You could see the Convolutional layers as a dimension reduction technique. Indeed, nearby pixels share a lot of covariance and ideally the features for a machine learning approach are independent.
If the convolutional operator is replaced by a specific convolutional operator were all the weights are $1/d^2$ (i.e. the... | Why does locally connected layer work in convolutional neural network? | You could see the Convolutional layers as a dimension reduction technique. Indeed, nearby pixels share a lot of covariance and ideally the features for a machine learning approach are independent.
If | Why does locally connected layer work in convolutional neural network?
You could see the Convolutional layers as a dimension reduction technique. Indeed, nearby pixels share a lot of covariance and ideally the features for a machine learning approach are independent.
If the convolutional operator is replaced by a spe... | Why does locally connected layer work in convolutional neural network?
You could see the Convolutional layers as a dimension reduction technique. Indeed, nearby pixels share a lot of covariance and ideally the features for a machine learning approach are independent.
If |
50,408 | Why does locally connected layer work in convolutional neural network? | As per Ian Goodfellow et al. from deeplearningbook:
Locally connected layers are useful when we know that each feature should be
a function of a small part of space, but there is no reason to think that the same
feature should occur across all of space. For example, if we want to tell if an image
is a picture o... | Why does locally connected layer work in convolutional neural network? | As per Ian Goodfellow et al. from deeplearningbook:
Locally connected layers are useful when we know that each feature should be
a function of a small part of space, but there is no reason to thin | Why does locally connected layer work in convolutional neural network?
As per Ian Goodfellow et al. from deeplearningbook:
Locally connected layers are useful when we know that each feature should be
a function of a small part of space, but there is no reason to think that the same
feature should occur across all... | Why does locally connected layer work in convolutional neural network?
As per Ian Goodfellow et al. from deeplearningbook:
Locally connected layers are useful when we know that each feature should be
a function of a small part of space, but there is no reason to thin |
50,409 | Should between-subject factors be included as random slopes for item in a mixed effects model? | See here. In short, "a model specifying random slopes for a between subjects variable would be unidentifiable." But you can still include within-subject factors as random slopes for subject RE. | Should between-subject factors be included as random slopes for item in a mixed effects model? | See here. In short, "a model specifying random slopes for a between subjects variable would be unidentifiable." But you can still include within-subject factors as random slopes for subject RE. | Should between-subject factors be included as random slopes for item in a mixed effects model?
See here. In short, "a model specifying random slopes for a between subjects variable would be unidentifiable." But you can still include within-subject factors as random slopes for subject RE. | Should between-subject factors be included as random slopes for item in a mixed effects model?
See here. In short, "a model specifying random slopes for a between subjects variable would be unidentifiable." But you can still include within-subject factors as random slopes for subject RE. |
50,410 | To what extent are convolutional neural networks inspired by biology? | The paper https://arxiv.org/pdf/1807.04587.pdf (July 2018) reports on some efforts to find artificial neural network learning algorithms that are biologically plausible. They focus mainly on backpropagation, but also discuss weight sharing. They review a lot of work by major researchers in the field and others.
They co... | To what extent are convolutional neural networks inspired by biology? | The paper https://arxiv.org/pdf/1807.04587.pdf (July 2018) reports on some efforts to find artificial neural network learning algorithms that are biologically plausible. They focus mainly on backpropa | To what extent are convolutional neural networks inspired by biology?
The paper https://arxiv.org/pdf/1807.04587.pdf (July 2018) reports on some efforts to find artificial neural network learning algorithms that are biologically plausible. They focus mainly on backpropagation, but also discuss weight sharing. They revi... | To what extent are convolutional neural networks inspired by biology?
The paper https://arxiv.org/pdf/1807.04587.pdf (July 2018) reports on some efforts to find artificial neural network learning algorithms that are biologically plausible. They focus mainly on backpropa |
50,411 | To what extent are convolutional neural networks inspired by biology? | Related to the paper linked here by @JWG, here is a lecture by Hinton regarding the same topic, also be sure you take a look into his lately explored notions on capsule networks
https://www.youtube.com/watch?v=rTawFwUvnLE
And in more general terms, Hinton is certainly one of my best first bets whenever trying to bridge... | To what extent are convolutional neural networks inspired by biology? | Related to the paper linked here by @JWG, here is a lecture by Hinton regarding the same topic, also be sure you take a look into his lately explored notions on capsule networks
https://www.youtube.co | To what extent are convolutional neural networks inspired by biology?
Related to the paper linked here by @JWG, here is a lecture by Hinton regarding the same topic, also be sure you take a look into his lately explored notions on capsule networks
https://www.youtube.com/watch?v=rTawFwUvnLE
And in more general terms, H... | To what extent are convolutional neural networks inspired by biology?
Related to the paper linked here by @JWG, here is a lecture by Hinton regarding the same topic, also be sure you take a look into his lately explored notions on capsule networks
https://www.youtube.co |
50,412 | In mathematical optimization, are sequential quadratic programming and sequential least squares programming the same thing? | Actually SQP and SLSQP sovles the same subproblem of Quadratic Programming (QP) (see subproblem here) on each algorithm step.
In SQP the problem of QP is solved by methods of Quadratic Programming.
In SLSQP to solve the problem of QP you should $LDL^{-1}$-factorize Lagrange Hessian and then solve a linear least squar... | In mathematical optimization, are sequential quadratic programming and sequential least squares prog | Actually SQP and SLSQP sovles the same subproblem of Quadratic Programming (QP) (see subproblem here) on each algorithm step.
In SQP the problem of QP is solved by methods of Quadratic Programming.
| In mathematical optimization, are sequential quadratic programming and sequential least squares programming the same thing?
Actually SQP and SLSQP sovles the same subproblem of Quadratic Programming (QP) (see subproblem here) on each algorithm step.
In SQP the problem of QP is solved by methods of Quadratic Programmin... | In mathematical optimization, are sequential quadratic programming and sequential least squares prog
Actually SQP and SLSQP sovles the same subproblem of Quadratic Programming (QP) (see subproblem here) on each algorithm step.
In SQP the problem of QP is solved by methods of Quadratic Programming.
|
50,413 | How can I obtain prediction intervals for survival prediction in the Cox model? | It's perhaps useful to note in your example that when age is taken to be the empirical mean, there is no difference in the plot.survfit output: e.g. plot(survfit(fit, newdata = data.frame(age=mean(ovarian$age)))) and plot(survfit(fit)) produce the same results. This is because the cox model using the hazard ratio to de... | How can I obtain prediction intervals for survival prediction in the Cox model? | It's perhaps useful to note in your example that when age is taken to be the empirical mean, there is no difference in the plot.survfit output: e.g. plot(survfit(fit, newdata = data.frame(age=mean(ova | How can I obtain prediction intervals for survival prediction in the Cox model?
It's perhaps useful to note in your example that when age is taken to be the empirical mean, there is no difference in the plot.survfit output: e.g. plot(survfit(fit, newdata = data.frame(age=mean(ovarian$age)))) and plot(survfit(fit)) prod... | How can I obtain prediction intervals for survival prediction in the Cox model?
It's perhaps useful to note in your example that when age is taken to be the empirical mean, there is no difference in the plot.survfit output: e.g. plot(survfit(fit, newdata = data.frame(age=mean(ova |
50,414 | Explanation of the 'free bits' technique for variational autoencoders | From what I can tell, and I'd love to be corrected as this seems quite interesting:
The `IAF' paper contains the relevant description of this 'free bits' method. In particular around equation (15). This identifies the term as relating to a modified objective function, "We then use the following objective, which ensur... | Explanation of the 'free bits' technique for variational autoencoders | From what I can tell, and I'd love to be corrected as this seems quite interesting:
The `IAF' paper contains the relevant description of this 'free bits' method. In particular around equation (15). | Explanation of the 'free bits' technique for variational autoencoders
From what I can tell, and I'd love to be corrected as this seems quite interesting:
The `IAF' paper contains the relevant description of this 'free bits' method. In particular around equation (15). This identifies the term as relating to a modified... | Explanation of the 'free bits' technique for variational autoencoders
From what I can tell, and I'd love to be corrected as this seems quite interesting:
The `IAF' paper contains the relevant description of this 'free bits' method. In particular around equation (15). |
50,415 | Best ANN Architecture for high-energy physics problem | Your architecture looks fine. I mean, it's straight out of MNIST lenet. It's a good solid network to start from. You can then evolve it over time, according to your loss curves, by adding capacity, ie layers, channels per layer, etc.
You could also consider adding dropout, for regularization.
As far as convergence...... | Best ANN Architecture for high-energy physics problem | Your architecture looks fine. I mean, it's straight out of MNIST lenet. It's a good solid network to start from. You can then evolve it over time, according to your loss curves, by adding capacity, | Best ANN Architecture for high-energy physics problem
Your architecture looks fine. I mean, it's straight out of MNIST lenet. It's a good solid network to start from. You can then evolve it over time, according to your loss curves, by adding capacity, ie layers, channels per layer, etc.
You could also consider adding... | Best ANN Architecture for high-energy physics problem
Your architecture looks fine. I mean, it's straight out of MNIST lenet. It's a good solid network to start from. You can then evolve it over time, according to your loss curves, by adding capacity, |
50,416 | Best ANN Architecture for high-energy physics problem | Thomas Russell
First of all it is very interesting problem to solve.
I think you should not merge features extracted from CNN and other variables. You can try training CNN end-to-end for predicting class scores and train a Neural Network using other variables for predicting class scores and then some how merge both pre... | Best ANN Architecture for high-energy physics problem | Thomas Russell
First of all it is very interesting problem to solve.
I think you should not merge features extracted from CNN and other variables. You can try training CNN end-to-end for predicting cl | Best ANN Architecture for high-energy physics problem
Thomas Russell
First of all it is very interesting problem to solve.
I think you should not merge features extracted from CNN and other variables. You can try training CNN end-to-end for predicting class scores and train a Neural Network using other variables for pr... | Best ANN Architecture for high-energy physics problem
Thomas Russell
First of all it is very interesting problem to solve.
I think you should not merge features extracted from CNN and other variables. You can try training CNN end-to-end for predicting cl |
50,417 | Probability one Weibull is less than another, given upper-bound | OK, I have made some progress.
The solution for this general kind of problem is described in this post:
https://math.stackexchange.com/questions/396386/finding-an-expression-for-the-probability-that-one-random-variable-is-less-than
Assume for simplicity that $X$ and $Y$ have respectively density functions $f_X(x)$ and... | Probability one Weibull is less than another, given upper-bound | OK, I have made some progress.
The solution for this general kind of problem is described in this post:
https://math.stackexchange.com/questions/396386/finding-an-expression-for-the-probability-that-o | Probability one Weibull is less than another, given upper-bound
OK, I have made some progress.
The solution for this general kind of problem is described in this post:
https://math.stackexchange.com/questions/396386/finding-an-expression-for-the-probability-that-one-random-variable-is-less-than
Assume for simplicity t... | Probability one Weibull is less than another, given upper-bound
OK, I have made some progress.
The solution for this general kind of problem is described in this post:
https://math.stackexchange.com/questions/396386/finding-an-expression-for-the-probability-that-o |
50,418 | how to use GLS with correlation structure to compare two temperature time series? | A way to answer your question (I want to be able to say if the shallow site is warmer than the deep one or not.) would be to work on the difference between the two time series SiteB - SiteA (A=deep, B= shallow).
Both time series are stationary. So the means of the time series are not time-dependent. Both time series ca... | how to use GLS with correlation structure to compare two temperature time series? | A way to answer your question (I want to be able to say if the shallow site is warmer than the deep one or not.) would be to work on the difference between the two time series SiteB - SiteA (A=deep, B | how to use GLS with correlation structure to compare two temperature time series?
A way to answer your question (I want to be able to say if the shallow site is warmer than the deep one or not.) would be to work on the difference between the two time series SiteB - SiteA (A=deep, B= shallow).
Both time series are stati... | how to use GLS with correlation structure to compare two temperature time series?
A way to answer your question (I want to be able to say if the shallow site is warmer than the deep one or not.) would be to work on the difference between the two time series SiteB - SiteA (A=deep, B |
50,419 | how to use GLS with correlation structure to compare two temperature time series? | The autoregressive correlations of order 1 of the residuals of model m3a of the two time series were pretty high (A: ca. 0.4 and B: ca. 0.6), and both are significant at 0.05 level. As such, the results from summary() are invalid.
The autocorrelation coefficients for the two ts can be visualized by:
acf2(residuals(m3a... | how to use GLS with correlation structure to compare two temperature time series? | The autoregressive correlations of order 1 of the residuals of model m3a of the two time series were pretty high (A: ca. 0.4 and B: ca. 0.6), and both are significant at 0.05 level. As such, the resul | how to use GLS with correlation structure to compare two temperature time series?
The autoregressive correlations of order 1 of the residuals of model m3a of the two time series were pretty high (A: ca. 0.4 and B: ca. 0.6), and both are significant at 0.05 level. As such, the results from summary() are invalid.
The aut... | how to use GLS with correlation structure to compare two temperature time series?
The autoregressive correlations of order 1 of the residuals of model m3a of the two time series were pretty high (A: ca. 0.4 and B: ca. 0.6), and both are significant at 0.05 level. As such, the resul |
50,420 | Why noisy data will benefit Bayesian? | Adding noise reduces the quality of Bayesian results as it does for Frequentist and Likelihoodist methods. It will also slow down the model. This can be seen with a simple, degenerate example.
Consider a case of data consisting of five points (1,1), (2,2), (3,3), (4,4) and (5,5). The slope is 1 and the intercept is ... | Why noisy data will benefit Bayesian? | Adding noise reduces the quality of Bayesian results as it does for Frequentist and Likelihoodist methods. It will also slow down the model. This can be seen with a simple, degenerate example.
Consi | Why noisy data will benefit Bayesian?
Adding noise reduces the quality of Bayesian results as it does for Frequentist and Likelihoodist methods. It will also slow down the model. This can be seen with a simple, degenerate example.
Consider a case of data consisting of five points (1,1), (2,2), (3,3), (4,4) and (5,5).... | Why noisy data will benefit Bayesian?
Adding noise reduces the quality of Bayesian results as it does for Frequentist and Likelihoodist methods. It will also slow down the model. This can be seen with a simple, degenerate example.
Consi |
50,421 | confidence intervals in linear regression | I am from a different domain, and use somewhat different language, but maybe this will help.
Imagine doing an experiment. $x$ is a set of given values, an "independent variable". Not random. For each of these values you measure a dependent variable, $y$. Presumably, $y$ depends on $x$ in a deterministic (non-random) wa... | confidence intervals in linear regression | I am from a different domain, and use somewhat different language, but maybe this will help.
Imagine doing an experiment. $x$ is a set of given values, an "independent variable". Not random. For each | confidence intervals in linear regression
I am from a different domain, and use somewhat different language, but maybe this will help.
Imagine doing an experiment. $x$ is a set of given values, an "independent variable". Not random. For each of these values you measure a dependent variable, $y$. Presumably, $y$ depends... | confidence intervals in linear regression
I am from a different domain, and use somewhat different language, but maybe this will help.
Imagine doing an experiment. $x$ is a set of given values, an "independent variable". Not random. For each |
50,422 | confidence intervals in linear regression | Error is the only random variable. The X's are assumed to fixed but if you assume linearity you can generalize to other values of X. Of course extrapolation is very risky and rarely justified. | confidence intervals in linear regression | Error is the only random variable. The X's are assumed to fixed but if you assume linearity you can generalize to other values of X. Of course extrapolation is very risky and rarely justified. | confidence intervals in linear regression
Error is the only random variable. The X's are assumed to fixed but if you assume linearity you can generalize to other values of X. Of course extrapolation is very risky and rarely justified. | confidence intervals in linear regression
Error is the only random variable. The X's are assumed to fixed but if you assume linearity you can generalize to other values of X. Of course extrapolation is very risky and rarely justified. |
50,423 | Real world examples of the sleeping beauty paradox | My candidate for a real-world analogue: "How likely is it that there is intelligent life elsewhere in the universe?"
To simplify things, assume that God picked the fundamental physical constants at random. Assume that there was a 50% chance of picking values which would result in a universe hostile to life, where intel... | Real world examples of the sleeping beauty paradox | My candidate for a real-world analogue: "How likely is it that there is intelligent life elsewhere in the universe?"
To simplify things, assume that God picked the fundamental physical constants at ra | Real world examples of the sleeping beauty paradox
My candidate for a real-world analogue: "How likely is it that there is intelligent life elsewhere in the universe?"
To simplify things, assume that God picked the fundamental physical constants at random. Assume that there was a 50% chance of picking values which woul... | Real world examples of the sleeping beauty paradox
My candidate for a real-world analogue: "How likely is it that there is intelligent life elsewhere in the universe?"
To simplify things, assume that God picked the fundamental physical constants at ra |
50,424 | Bernoulli NB vs MultiNomial NB, How to choose among different NB algorithms? | The variant of Naive Bayes you use depends on the data. If your data consists of counts, the multinomial distribution may be an appropriate distribution for the likelihood, and thus multinomial Naive Bayes is appropriate.
Likewise, if your data points come from distribution $X$, use the likelihood for $X$ for Naive Ba... | Bernoulli NB vs MultiNomial NB, How to choose among different NB algorithms? | The variant of Naive Bayes you use depends on the data. If your data consists of counts, the multinomial distribution may be an appropriate distribution for the likelihood, and thus multinomial Naive | Bernoulli NB vs MultiNomial NB, How to choose among different NB algorithms?
The variant of Naive Bayes you use depends on the data. If your data consists of counts, the multinomial distribution may be an appropriate distribution for the likelihood, and thus multinomial Naive Bayes is appropriate.
Likewise, if your da... | Bernoulli NB vs MultiNomial NB, How to choose among different NB algorithms?
The variant of Naive Bayes you use depends on the data. If your data consists of counts, the multinomial distribution may be an appropriate distribution for the likelihood, and thus multinomial Naive |
50,425 | Bernoulli NB vs MultiNomial NB, How to choose among different NB algorithms? | As Ryan Rosario states, which model you choose depends on the kind of data you have. You may wish to read this paper by McCallum and Nigam from 1998 on the difference between the multinomial and bernoulli naive Bayes models:
"A Comparison of Event Models for Naive Bayes Text Classification"
http://www.cs.cmu.edu/~kniga... | Bernoulli NB vs MultiNomial NB, How to choose among different NB algorithms? | As Ryan Rosario states, which model you choose depends on the kind of data you have. You may wish to read this paper by McCallum and Nigam from 1998 on the difference between the multinomial and berno | Bernoulli NB vs MultiNomial NB, How to choose among different NB algorithms?
As Ryan Rosario states, which model you choose depends on the kind of data you have. You may wish to read this paper by McCallum and Nigam from 1998 on the difference between the multinomial and bernoulli naive Bayes models:
"A Comparison of E... | Bernoulli NB vs MultiNomial NB, How to choose among different NB algorithms?
As Ryan Rosario states, which model you choose depends on the kind of data you have. You may wish to read this paper by McCallum and Nigam from 1998 on the difference between the multinomial and berno |
50,426 | Model/variable selection for time series | If the focus is on time-series and forecasting, then I would only consider rolling CV. When working with time-series it is critical to exclude any innovative (unknown) process from the fit.
ICs estimate variance by penalizing the model fit (through degrees of freedom or other variables). These formulas were designed wh... | Model/variable selection for time series | If the focus is on time-series and forecasting, then I would only consider rolling CV. When working with time-series it is critical to exclude any innovative (unknown) process from the fit.
ICs estima | Model/variable selection for time series
If the focus is on time-series and forecasting, then I would only consider rolling CV. When working with time-series it is critical to exclude any innovative (unknown) process from the fit.
ICs estimate variance by penalizing the model fit (through degrees of freedom or other va... | Model/variable selection for time series
If the focus is on time-series and forecasting, then I would only consider rolling CV. When working with time-series it is critical to exclude any innovative (unknown) process from the fit.
ICs estima |
50,427 | Marginal distribution of the difference of two elements of a Dirichlet distributed vector | Analysis
The thread at Construction of Dirichlet distribution with Gamma distribution shows that the Dirichlet distribution with parameters $(\alpha_1, \alpha_2, \ldots, \alpha_{n+1})$ arises as the distribution of the ratios
$$X_i=\frac{Y_i}{Y_1+Y_2+\cdots + Y_{n+1}},$$
$i=1, 2, \ldots, n$ where the $Y_j$ are independ... | Marginal distribution of the difference of two elements of a Dirichlet distributed vector | Analysis
The thread at Construction of Dirichlet distribution with Gamma distribution shows that the Dirichlet distribution with parameters $(\alpha_1, \alpha_2, \ldots, \alpha_{n+1})$ arises as the d | Marginal distribution of the difference of two elements of a Dirichlet distributed vector
Analysis
The thread at Construction of Dirichlet distribution with Gamma distribution shows that the Dirichlet distribution with parameters $(\alpha_1, \alpha_2, \ldots, \alpha_{n+1})$ arises as the distribution of the ratios
$$X_... | Marginal distribution of the difference of two elements of a Dirichlet distributed vector
Analysis
The thread at Construction of Dirichlet distribution with Gamma distribution shows that the Dirichlet distribution with parameters $(\alpha_1, \alpha_2, \ldots, \alpha_{n+1})$ arises as the d |
50,428 | Yearly Aggregated Loss Distribution (operational risk) | Since you are new to this, I think it's best to walk through an example. Let's consider the case of a single risk $Z$ (i.e. a certain type of operational risk).
The Loss Distribution Approach can be described as:
$$Z=\sum_{i=1}^{N}X_{i}$$
where $N$ is the number of events (frequency) over one year and $X_{i}$ is the se... | Yearly Aggregated Loss Distribution (operational risk) | Since you are new to this, I think it's best to walk through an example. Let's consider the case of a single risk $Z$ (i.e. a certain type of operational risk).
The Loss Distribution Approach can be d | Yearly Aggregated Loss Distribution (operational risk)
Since you are new to this, I think it's best to walk through an example. Let's consider the case of a single risk $Z$ (i.e. a certain type of operational risk).
The Loss Distribution Approach can be described as:
$$Z=\sum_{i=1}^{N}X_{i}$$
where $N$ is the number of... | Yearly Aggregated Loss Distribution (operational risk)
Since you are new to this, I think it's best to walk through an example. Let's consider the case of a single risk $Z$ (i.e. a certain type of operational risk).
The Loss Distribution Approach can be d |
50,429 | Yearly Aggregated Loss Distribution (operational risk) | Your approach is as silly as everyone else's. Look at what the FRB is doing to forecast losses for CCAR Banks, their approach is described in: Dodd-Frank Act Stress Test 2016: Supervisory Stress Test Methodology and Results, June 2016. Read “Operational Risk Model Enhancement” section in Box 1 and “Losses Related to Op... | Yearly Aggregated Loss Distribution (operational risk) | Your approach is as silly as everyone else's. Look at what the FRB is doing to forecast losses for CCAR Banks, their approach is described in: Dodd-Frank Act Stress Test 2016: Supervisory Stress Test | Yearly Aggregated Loss Distribution (operational risk)
Your approach is as silly as everyone else's. Look at what the FRB is doing to forecast losses for CCAR Banks, their approach is described in: Dodd-Frank Act Stress Test 2016: Supervisory Stress Test Methodology and Results, June 2016. Read “Operational Risk Model ... | Yearly Aggregated Loss Distribution (operational risk)
Your approach is as silly as everyone else's. Look at what the FRB is doing to forecast losses for CCAR Banks, their approach is described in: Dodd-Frank Act Stress Test 2016: Supervisory Stress Test |
50,430 | Yearly Aggregated Loss Distribution (operational risk) | We have seen this question ( or one like this ) before . It involves using daily data to compute aggregated forecasts yielding the probability of making a goal. Look at Predict number of users for a discussion of how Proctor & Gamble phrased the question. You might also look at http://www.autobox.com/cms/index.php/blog... | Yearly Aggregated Loss Distribution (operational risk) | We have seen this question ( or one like this ) before . It involves using daily data to compute aggregated forecasts yielding the probability of making a goal. Look at Predict number of users for a d | Yearly Aggregated Loss Distribution (operational risk)
We have seen this question ( or one like this ) before . It involves using daily data to compute aggregated forecasts yielding the probability of making a goal. Look at Predict number of users for a discussion of how Proctor & Gamble phrased the question. You might... | Yearly Aggregated Loss Distribution (operational risk)
We have seen this question ( or one like this ) before . It involves using daily data to compute aggregated forecasts yielding the probability of making a goal. Look at Predict number of users for a d |
50,431 | Surface Fit Using Tensor Product of B-Splines | Not vectorizing your response matrix $Y$ is the way to go;
B = ginv(t(C) %*% C) %*% t(C) %*% Y #OLS
pred = C%*%B #Predictions
surface3d(x,x, pred, col = "green") #Plot | Surface Fit Using Tensor Product of B-Splines | Not vectorizing your response matrix $Y$ is the way to go;
B = ginv(t(C) %*% C) %*% t(C) %*% Y #OLS
pred = C%*%B #Predictions
surface3d(x,x, pred, col = "green") #Plot | Surface Fit Using Tensor Product of B-Splines
Not vectorizing your response matrix $Y$ is the way to go;
B = ginv(t(C) %*% C) %*% t(C) %*% Y #OLS
pred = C%*%B #Predictions
surface3d(x,x, pred, col = "green") #Plot | Surface Fit Using Tensor Product of B-Splines
Not vectorizing your response matrix $Y$ is the way to go;
B = ginv(t(C) %*% C) %*% t(C) %*% Y #OLS
pred = C%*%B #Predictions
surface3d(x,x, pred, col = "green") #Plot |
50,432 | How to randomly generate a positive semidefinite matrix subject to Loewner constraint? | Let $\mathbb S_n$ be the set of $n \times n$ symmetric matrices. Given positive semidefinite matrices $\mathrm A, \mathrm B \in \mathbb S_n$, the following (convex) set
$$\{ \mathrm X \in \mathbb S_n \mid \mathrm A \preceq \mathrm X \preceq \mathrm B \}$$
is a spectrahedron. To sample from spectrahedra, take a look at ... | How to randomly generate a positive semidefinite matrix subject to Loewner constraint? | Let $\mathbb S_n$ be the set of $n \times n$ symmetric matrices. Given positive semidefinite matrices $\mathrm A, \mathrm B \in \mathbb S_n$, the following (convex) set
$$\{ \mathrm X \in \mathbb S_n | How to randomly generate a positive semidefinite matrix subject to Loewner constraint?
Let $\mathbb S_n$ be the set of $n \times n$ symmetric matrices. Given positive semidefinite matrices $\mathrm A, \mathrm B \in \mathbb S_n$, the following (convex) set
$$\{ \mathrm X \in \mathbb S_n \mid \mathrm A \preceq \mathrm X ... | How to randomly generate a positive semidefinite matrix subject to Loewner constraint?
Let $\mathbb S_n$ be the set of $n \times n$ symmetric matrices. Given positive semidefinite matrices $\mathrm A, \mathrm B \in \mathbb S_n$, the following (convex) set
$$\{ \mathrm X \in \mathbb S_n |
50,433 | P-value distribution under alternative hypothesis is stochastically smaller than uniform | I assume that this is a self-study question, so I will not give a full explanation, but rather some hints
Assuming you know
What "stochastically smaller" means (see Wikipedia)
And you are able to interpret the difference of two cumulative distribution such as in the figure above above (note that the red line is the u... | P-value distribution under alternative hypothesis is stochastically smaller than uniform | I assume that this is a self-study question, so I will not give a full explanation, but rather some hints
Assuming you know
What "stochastically smaller" means (see Wikipedia)
And you are able to in | P-value distribution under alternative hypothesis is stochastically smaller than uniform
I assume that this is a self-study question, so I will not give a full explanation, but rather some hints
Assuming you know
What "stochastically smaller" means (see Wikipedia)
And you are able to interpret the difference of two c... | P-value distribution under alternative hypothesis is stochastically smaller than uniform
I assume that this is a self-study question, so I will not give a full explanation, but rather some hints
Assuming you know
What "stochastically smaller" means (see Wikipedia)
And you are able to in |
50,434 | Confidence intervals of bounded variable | This is a later answer but perhaps may be useful to someone. I have an R package on github (mlisi) with a set of convenient functions, including one that calculate boostrapped confidence intervals using the bias-corrected and accelerated method (Efron, 1987).
> set.seed(10)
> data = runif(1000, min=0, max=1)
> library(... | Confidence intervals of bounded variable | This is a later answer but perhaps may be useful to someone. I have an R package on github (mlisi) with a set of convenient functions, including one that calculate boostrapped confidence intervals usi | Confidence intervals of bounded variable
This is a later answer but perhaps may be useful to someone. I have an R package on github (mlisi) with a set of convenient functions, including one that calculate boostrapped confidence intervals using the bias-corrected and accelerated method (Efron, 1987).
> set.seed(10)
> da... | Confidence intervals of bounded variable
This is a later answer but perhaps may be useful to someone. I have an R package on github (mlisi) with a set of convenient functions, including one that calculate boostrapped confidence intervals usi |
50,435 | Confidence intervals of bounded variable | Your best best here would be to use bootstrapped CIs instead of parametric CIs. Here is a contrived example to show when parametric CIs would give impossible results but bootstrap CIs do not:
> # Simulate Bounded Data
> set.seed(10)
> n <- 5
> data <- rnorm(n, mean = 1, sd = 0.5)
> data[data > 1] <- 1
>
> # Sample Me... | Confidence intervals of bounded variable | Your best best here would be to use bootstrapped CIs instead of parametric CIs. Here is a contrived example to show when parametric CIs would give impossible results but bootstrap CIs do not:
> # Simu | Confidence intervals of bounded variable
Your best best here would be to use bootstrapped CIs instead of parametric CIs. Here is a contrived example to show when parametric CIs would give impossible results but bootstrap CIs do not:
> # Simulate Bounded Data
> set.seed(10)
> n <- 5
> data <- rnorm(n, mean = 1, sd = 0.... | Confidence intervals of bounded variable
Your best best here would be to use bootstrapped CIs instead of parametric CIs. Here is a contrived example to show when parametric CIs would give impossible results but bootstrap CIs do not:
> # Simu |
50,436 | Similarity probabilities in SNE vs t-SNE | I think the paper defines the joint distribution (not the conditional distribution!) as
$$p_{ij} = \frac{\exp(-||x_{i} - x_{j}||/2\sigma^{2})}{\sum_{k \neq l}{\exp(-||x_{k} - x_{l}||/2\sigma^{2})}},$$
but they do not use it and instead define $$p_{ij}=\frac{p_{j|i}+p_{i|j}}{2}.$$
As mentioned in the paper the original... | Similarity probabilities in SNE vs t-SNE | I think the paper defines the joint distribution (not the conditional distribution!) as
$$p_{ij} = \frac{\exp(-||x_{i} - x_{j}||/2\sigma^{2})}{\sum_{k \neq l}{\exp(-||x_{k} - x_{l}||/2\sigma^{2})}},$ | Similarity probabilities in SNE vs t-SNE
I think the paper defines the joint distribution (not the conditional distribution!) as
$$p_{ij} = \frac{\exp(-||x_{i} - x_{j}||/2\sigma^{2})}{\sum_{k \neq l}{\exp(-||x_{k} - x_{l}||/2\sigma^{2})}},$$
but they do not use it and instead define $$p_{ij}=\frac{p_{j|i}+p_{i|j}}{2}.... | Similarity probabilities in SNE vs t-SNE
I think the paper defines the joint distribution (not the conditional distribution!) as
$$p_{ij} = \frac{\exp(-||x_{i} - x_{j}||/2\sigma^{2})}{\sum_{k \neq l}{\exp(-||x_{k} - x_{l}||/2\sigma^{2})}},$ |
50,437 | If missing data process is known and it is MNAR, is it possible to get an unbiased estimate of parameter? | This is an interesting question.
First I will show that $r>0$. If $r=0$, then there is no observed data, and the likelihood function is no longer concave, thus this statistical problem is not well defined. Given $r>0$, $E(1/r)$ should be finite.
Let $p=\mathrm{Pr}(y_i<c) = 1-\mathrm{exp}(-c/\theta)$. We have $$E(1/r) ... | If missing data process is known and it is MNAR, is it possible to get an unbiased estimate of param | This is an interesting question.
First I will show that $r>0$. If $r=0$, then there is no observed data, and the likelihood function is no longer concave, thus this statistical problem is not well de | If missing data process is known and it is MNAR, is it possible to get an unbiased estimate of parameter?
This is an interesting question.
First I will show that $r>0$. If $r=0$, then there is no observed data, and the likelihood function is no longer concave, thus this statistical problem is not well defined. Given $... | If missing data process is known and it is MNAR, is it possible to get an unbiased estimate of param
This is an interesting question.
First I will show that $r>0$. If $r=0$, then there is no observed data, and the likelihood function is no longer concave, thus this statistical problem is not well de |
50,438 | Expected value of $\dfrac 1 {I(y_1<c) + I(y_2<c)}$, where $y_1$ and $y_2$ are i.i.d. random variables with exponential distribution | This random variable has a positive probability to be infinite, therefore its expectation is $+\infty$. | Expected value of $\dfrac 1 {I(y_1<c) + I(y_2<c)}$, where $y_1$ and $y_2$ are i.i.d. random variable | This random variable has a positive probability to be infinite, therefore its expectation is $+\infty$. | Expected value of $\dfrac 1 {I(y_1<c) + I(y_2<c)}$, where $y_1$ and $y_2$ are i.i.d. random variables with exponential distribution
This random variable has a positive probability to be infinite, therefore its expectation is $+\infty$. | Expected value of $\dfrac 1 {I(y_1<c) + I(y_2<c)}$, where $y_1$ and $y_2$ are i.i.d. random variable
This random variable has a positive probability to be infinite, therefore its expectation is $+\infty$. |
50,439 | Relationship between binomial regression link function and goodness-of-fit tests [now with link to R code] | I've been able to prove both effects shown here.
Let the model matrix be $X$, an $N \times (p+1)$ matrix whose first column is the intercept column (all ones) and whose $1 \times (p+1)$ columns are $x_k^T$. The fitted value from the regression is $p_k = g(x_k^T \hat\theta)$, with the link function $g(\eta)$.
Pearson te... | Relationship between binomial regression link function and goodness-of-fit tests [now with link to R | I've been able to prove both effects shown here.
Let the model matrix be $X$, an $N \times (p+1)$ matrix whose first column is the intercept column (all ones) and whose $1 \times (p+1)$ columns are $x | Relationship between binomial regression link function and goodness-of-fit tests [now with link to R code]
I've been able to prove both effects shown here.
Let the model matrix be $X$, an $N \times (p+1)$ matrix whose first column is the intercept column (all ones) and whose $1 \times (p+1)$ columns are $x_k^T$. The fi... | Relationship between binomial regression link function and goodness-of-fit tests [now with link to R
I've been able to prove both effects shown here.
Let the model matrix be $X$, an $N \times (p+1)$ matrix whose first column is the intercept column (all ones) and whose $1 \times (p+1)$ columns are $x |
50,440 | Unsupervised outlier detection in 2D space | Your task seems to be rather a clustering than an outlier detection task.
In the following, I use this popular data set of User locations (Joensuu).
Running OPTICS with the parameters
-dbc.in /tmp/MopsiLocations2012-Joensuu.txt
-algorithm clustering.optics.OPTICSXi -opticsxi.xi 0.05
-algorithm.distancefunction geo.LngL... | Unsupervised outlier detection in 2D space | Your task seems to be rather a clustering than an outlier detection task.
In the following, I use this popular data set of User locations (Joensuu).
Running OPTICS with the parameters
-dbc.in /tmp/Mop | Unsupervised outlier detection in 2D space
Your task seems to be rather a clustering than an outlier detection task.
In the following, I use this popular data set of User locations (Joensuu).
Running OPTICS with the parameters
-dbc.in /tmp/MopsiLocations2012-Joensuu.txt
-algorithm clustering.optics.OPTICSXi -opticsxi.x... | Unsupervised outlier detection in 2D space
Your task seems to be rather a clustering than an outlier detection task.
In the following, I use this popular data set of User locations (Joensuu).
Running OPTICS with the parameters
-dbc.in /tmp/Mop |
50,441 | Unsupervised outlier detection in 2D space | To answer Edit 2 of this old question -- one way would be to compute the Mahalanobis distance for each point to the center of the cluster, then delete those above a certain cutoff distance. | Unsupervised outlier detection in 2D space | To answer Edit 2 of this old question -- one way would be to compute the Mahalanobis distance for each point to the center of the cluster, then delete those above a certain cutoff distance. | Unsupervised outlier detection in 2D space
To answer Edit 2 of this old question -- one way would be to compute the Mahalanobis distance for each point to the center of the cluster, then delete those above a certain cutoff distance. | Unsupervised outlier detection in 2D space
To answer Edit 2 of this old question -- one way would be to compute the Mahalanobis distance for each point to the center of the cluster, then delete those above a certain cutoff distance. |
50,442 | Is it good practice to perform model parameter tuning on a random subsampling of a large dataset? | This question is really broad. Depends on the data and model, it can be a good practice and can be bad.
The overall idea is to think about the "complexity of data and model". We may need to review Bias and Variance trade-off, i.e., when under-fitting and over-fitting will happen and how to detect it.
How to know if a l... | Is it good practice to perform model parameter tuning on a random subsampling of a large dataset? | This question is really broad. Depends on the data and model, it can be a good practice and can be bad.
The overall idea is to think about the "complexity of data and model". We may need to review Bia | Is it good practice to perform model parameter tuning on a random subsampling of a large dataset?
This question is really broad. Depends on the data and model, it can be a good practice and can be bad.
The overall idea is to think about the "complexity of data and model". We may need to review Bias and Variance trade-o... | Is it good practice to perform model parameter tuning on a random subsampling of a large dataset?
This question is really broad. Depends on the data and model, it can be a good practice and can be bad.
The overall idea is to think about the "complexity of data and model". We may need to review Bia |
50,443 | Inequality regarding expectation of function of a random variable | I don't know how to answer this in general but here's something. Maybe this will give you or someone else some ideas if nothing else.
Let us assume that $X$ belongs to the one-parameter exponential family with natural parameter $\theta$, so that
$$
f(x; \theta) = \exp \left( x \theta - \kappa(\theta) + c(x) \right)
$$... | Inequality regarding expectation of function of a random variable | I don't know how to answer this in general but here's something. Maybe this will give you or someone else some ideas if nothing else.
Let us assume that $X$ belongs to the one-parameter exponential fa | Inequality regarding expectation of function of a random variable
I don't know how to answer this in general but here's something. Maybe this will give you or someone else some ideas if nothing else.
Let us assume that $X$ belongs to the one-parameter exponential family with natural parameter $\theta$, so that
$$
f(x;... | Inequality regarding expectation of function of a random variable
I don't know how to answer this in general but here's something. Maybe this will give you or someone else some ideas if nothing else.
Let us assume that $X$ belongs to the one-parameter exponential fa |
50,444 | Interpreting correlations between two time-series | You seem to have looked at spurious results by looking at correlations of absolute values rather than correlation of changes.
If so, then see these two links for an explanation (ignore otherwise): quant.stackexchange.com/questions/489/correlation-between-prices-or-returns & stats.stackexchange.com/a/133171/114856.
I ... | Interpreting correlations between two time-series | You seem to have looked at spurious results by looking at correlations of absolute values rather than correlation of changes.
If so, then see these two links for an explanation (ignore otherwise): qu | Interpreting correlations between two time-series
You seem to have looked at spurious results by looking at correlations of absolute values rather than correlation of changes.
If so, then see these two links for an explanation (ignore otherwise): quant.stackexchange.com/questions/489/correlation-between-prices-or-retu... | Interpreting correlations between two time-series
You seem to have looked at spurious results by looking at correlations of absolute values rather than correlation of changes.
If so, then see these two links for an explanation (ignore otherwise): qu |
50,445 | Time series forecasting using statistical tools | Apparently the only information you have is the numbers of days and the number of news articles published on the feed.
I think what you are really asking is "Is this news feed worth my effort to poll?" and the desired answer is "Yes or No".
Therefore, you are actually wanting to perform a logistic regression. The r... | Time series forecasting using statistical tools | Apparently the only information you have is the numbers of days and the number of news articles published on the feed.
I think what you are really asking is "Is this news feed worth my effort to poll | Time series forecasting using statistical tools
Apparently the only information you have is the numbers of days and the number of news articles published on the feed.
I think what you are really asking is "Is this news feed worth my effort to poll?" and the desired answer is "Yes or No".
Therefore, you are actually ... | Time series forecasting using statistical tools
Apparently the only information you have is the numbers of days and the number of news articles published on the feed.
I think what you are really asking is "Is this news feed worth my effort to poll |
50,446 | Time series forecasting using statistical tools | Please don't get upset, I'm a newbie. My idea is even simpler than yours.
So you have to poll in a smart way, that is when the probability there are news articles on the feed is higher.
In my opinion the point is: "What is the probability to get new articles from $feed_n$ if today I poll it ?", if the probability is li... | Time series forecasting using statistical tools | Please don't get upset, I'm a newbie. My idea is even simpler than yours.
So you have to poll in a smart way, that is when the probability there are news articles on the feed is higher.
In my opinion | Time series forecasting using statistical tools
Please don't get upset, I'm a newbie. My idea is even simpler than yours.
So you have to poll in a smart way, that is when the probability there are news articles on the feed is higher.
In my opinion the point is: "What is the probability to get new articles from $feed_n$... | Time series forecasting using statistical tools
Please don't get upset, I'm a newbie. My idea is even simpler than yours.
So you have to poll in a smart way, that is when the probability there are news articles on the feed is higher.
In my opinion |
50,447 | Does the property of equivariance to translation of convolution layers help to learn translation-invariant features? [duplicate] | What causes convolutional neural networks to be somewhat translation invariant is the max pooling. Each neuron has a receptive field in the original image. For example, if you have two convolutional layers with stride 1 and one 2x2 max pooling step in between,
That is, input image --> C3x3/1 --> M2x2/2 --> C3x3/1 --> o... | Does the property of equivariance to translation of convolution layers help to learn translation-inv | What causes convolutional neural networks to be somewhat translation invariant is the max pooling. Each neuron has a receptive field in the original image. For example, if you have two convolutional l | Does the property of equivariance to translation of convolution layers help to learn translation-invariant features? [duplicate]
What causes convolutional neural networks to be somewhat translation invariant is the max pooling. Each neuron has a receptive field in the original image. For example, if you have two convol... | Does the property of equivariance to translation of convolution layers help to learn translation-inv
What causes convolutional neural networks to be somewhat translation invariant is the max pooling. Each neuron has a receptive field in the original image. For example, if you have two convolutional l |
50,448 | Does the property of equivariance to translation of convolution layers help to learn translation-invariant features? [duplicate] | I think the equivariance property does carry over consecutive convolutional layers if you had a chain of ConvNet without anything in between. But in practice, you have a relu or a pool layer and so that equivariant property doesn't hold across layers.
For Pooling, I think it only helps with small translations in the i... | Does the property of equivariance to translation of convolution layers help to learn translation-inv | I think the equivariance property does carry over consecutive convolutional layers if you had a chain of ConvNet without anything in between. But in practice, you have a relu or a pool layer and so th | Does the property of equivariance to translation of convolution layers help to learn translation-invariant features? [duplicate]
I think the equivariance property does carry over consecutive convolutional layers if you had a chain of ConvNet without anything in between. But in practice, you have a relu or a pool layer ... | Does the property of equivariance to translation of convolution layers help to learn translation-inv
I think the equivariance property does carry over consecutive convolutional layers if you had a chain of ConvNet without anything in between. But in practice, you have a relu or a pool layer and so th |
50,449 | Complexity of a random forest with respect to maximum depth | For smaller data sets as simulated below the process should be linear. As pointed out by @EngrStudent, it may be an issue of L1, L2 and RAM clock speed. As model complexity increases the random forest algorithm probably cannot compute the entire tree(...or sub branch of tree) in L1 and/or L2 cache.
I tried to run a sim... | Complexity of a random forest with respect to maximum depth | For smaller data sets as simulated below the process should be linear. As pointed out by @EngrStudent, it may be an issue of L1, L2 and RAM clock speed. As model complexity increases the random forest | Complexity of a random forest with respect to maximum depth
For smaller data sets as simulated below the process should be linear. As pointed out by @EngrStudent, it may be an issue of L1, L2 and RAM clock speed. As model complexity increases the random forest algorithm probably cannot compute the entire tree(...or sub... | Complexity of a random forest with respect to maximum depth
For smaller data sets as simulated below the process should be linear. As pointed out by @EngrStudent, it may be an issue of L1, L2 and RAM clock speed. As model complexity increases the random forest |
50,450 | How to calculate a multiple correlation with non-negative constraints on the linear model's parameters? | If I understand this right, you can estimate a multiple regression model with non-negativity restrictions on the coefficients (in R, this can be done with, for instance,the CRAN package nnls), and then use the R-squared from that fit. There might well be some similar functions in python. | How to calculate a multiple correlation with non-negative constraints on the linear model's paramete | If I understand this right, you can estimate a multiple regression model with non-negativity restrictions on the coefficients (in R, this can be done with, for instance,the CRAN package nnls), and the | How to calculate a multiple correlation with non-negative constraints on the linear model's parameters?
If I understand this right, you can estimate a multiple regression model with non-negativity restrictions on the coefficients (in R, this can be done with, for instance,the CRAN package nnls), and then use the R-squa... | How to calculate a multiple correlation with non-negative constraints on the linear model's paramete
If I understand this right, you can estimate a multiple regression model with non-negativity restrictions on the coefficients (in R, this can be done with, for instance,the CRAN package nnls), and the |
50,451 | How to calculate a multiple correlation with non-negative constraints on the linear model's parameters? | Core Answer
Echoing the answer by Kjetil, you could approach this using non-negative least squares followed by calculating the $R^2$ for the fitted model.
In Python you can use scipy.optimize.nnls.
Example 1
Here is an example usage adapted from the documentation:
import numpy as np
from scipy.optimize import nnls
# M... | How to calculate a multiple correlation with non-negative constraints on the linear model's paramete | Core Answer
Echoing the answer by Kjetil, you could approach this using non-negative least squares followed by calculating the $R^2$ for the fitted model.
In Python you can use scipy.optimize.nnls.
Ex | How to calculate a multiple correlation with non-negative constraints on the linear model's parameters?
Core Answer
Echoing the answer by Kjetil, you could approach this using non-negative least squares followed by calculating the $R^2$ for the fitted model.
In Python you can use scipy.optimize.nnls.
Example 1
Here is ... | How to calculate a multiple correlation with non-negative constraints on the linear model's paramete
Core Answer
Echoing the answer by Kjetil, you could approach this using non-negative least squares followed by calculating the $R^2$ for the fitted model.
In Python you can use scipy.optimize.nnls.
Ex |
50,452 | ADF vs. DF what is the difference between augmented and the standard Dickey-Fuller test? | For future reference, the book referenced in Richard Hardy comment has the answer.
The book says, and I quote:
The unit root test described above are valid if the time series $y_t$ is well characterized by an AR(1) with white noise errors. Many financial time series, however, have a more complicated dynamic structure ... | ADF vs. DF what is the difference between augmented and the standard Dickey-Fuller test? | For future reference, the book referenced in Richard Hardy comment has the answer.
The book says, and I quote:
The unit root test described above are valid if the time series $y_t$ is well characteri | ADF vs. DF what is the difference between augmented and the standard Dickey-Fuller test?
For future reference, the book referenced in Richard Hardy comment has the answer.
The book says, and I quote:
The unit root test described above are valid if the time series $y_t$ is well characterized by an AR(1) with white nois... | ADF vs. DF what is the difference between augmented and the standard Dickey-Fuller test?
For future reference, the book referenced in Richard Hardy comment has the answer.
The book says, and I quote:
The unit root test described above are valid if the time series $y_t$ is well characteri |
50,453 | How to calculate likelihood for a mixture model with missing data? | A possible completion for your model (as far as I understand it without proper mathematical notations) is the hierarchical structure
Generate index $\iota$ taking value $i$ with probability $\pi_i$
Generate positive integer $m$ from a fixed distribution, e.g., a shifted Poisson $1+\mathcal{P}(1)$
Generate $m$ iid valu... | How to calculate likelihood for a mixture model with missing data? | A possible completion for your model (as far as I understand it without proper mathematical notations) is the hierarchical structure
Generate index $\iota$ taking value $i$ with probability $\pi_i$
G | How to calculate likelihood for a mixture model with missing data?
A possible completion for your model (as far as I understand it without proper mathematical notations) is the hierarchical structure
Generate index $\iota$ taking value $i$ with probability $\pi_i$
Generate positive integer $m$ from a fixed distributio... | How to calculate likelihood for a mixture model with missing data?
A possible completion for your model (as far as I understand it without proper mathematical notations) is the hierarchical structure
Generate index $\iota$ taking value $i$ with probability $\pi_i$
G |
50,454 | Does LSTM Eliminate Need for Input Lags? | I believe it is actually pretty clear what you mean by the term input lags, but I will state explicitly.
When doing a regression problem with an LSTM, a input signal $ \mathbf{x} \in \mathbb{R}^{n \times t \times c_1 } $ is used to predict another signal $ \mathbf{y} \in \mathbb{R}^{n \times t \times c_2} $. For simpli... | Does LSTM Eliminate Need for Input Lags? | I believe it is actually pretty clear what you mean by the term input lags, but I will state explicitly.
When doing a regression problem with an LSTM, a input signal $ \mathbf{x} \in \mathbb{R}^{n \ti | Does LSTM Eliminate Need for Input Lags?
I believe it is actually pretty clear what you mean by the term input lags, but I will state explicitly.
When doing a regression problem with an LSTM, a input signal $ \mathbf{x} \in \mathbb{R}^{n \times t \times c_1 } $ is used to predict another signal $ \mathbf{y} \in \mathbb... | Does LSTM Eliminate Need for Input Lags?
I believe it is actually pretty clear what you mean by the term input lags, but I will state explicitly.
When doing a regression problem with an LSTM, a input signal $ \mathbf{x} \in \mathbb{R}^{n \ti |
50,455 | Does LSTM Eliminate Need for Input Lags? | No, it doesn't eliminate that need. Sometimes people use a bi-directional LSTM to get information from both sides of a sample before making a prediction. In that case, you wouldn't have to do an input lag. | Does LSTM Eliminate Need for Input Lags? | No, it doesn't eliminate that need. Sometimes people use a bi-directional LSTM to get information from both sides of a sample before making a prediction. In that case, you wouldn't have to do an input | Does LSTM Eliminate Need for Input Lags?
No, it doesn't eliminate that need. Sometimes people use a bi-directional LSTM to get information from both sides of a sample before making a prediction. In that case, you wouldn't have to do an input lag. | Does LSTM Eliminate Need for Input Lags?
No, it doesn't eliminate that need. Sometimes people use a bi-directional LSTM to get information from both sides of a sample before making a prediction. In that case, you wouldn't have to do an input |
50,456 | Neural networks bounded output | A trick for bounded output range is to scale the target values between (0,1) and use sigmoid output + binary cross-entropy loss.
This is often used for image data, where all the pixel values are between (0,255).
Say $a=wh+b$ is the activation of the last layer,
for sigmoid output + binary cross-entropy loss
$$E(a,t')=t... | Neural networks bounded output | A trick for bounded output range is to scale the target values between (0,1) and use sigmoid output + binary cross-entropy loss.
This is often used for image data, where all the pixel values are betwe | Neural networks bounded output
A trick for bounded output range is to scale the target values between (0,1) and use sigmoid output + binary cross-entropy loss.
This is often used for image data, where all the pixel values are between (0,255).
Say $a=wh+b$ is the activation of the last layer,
for sigmoid output + binary... | Neural networks bounded output
A trick for bounded output range is to scale the target values between (0,1) and use sigmoid output + binary cross-entropy loss.
This is often used for image data, where all the pixel values are betwe |
50,457 | Standardizing skewed distributions for visualisation alongside others | It may help if you can provide examples of what you've got and what you're going for.
For unknown distributions, it's hard to beat box plots. Not that they're perfect, but they are well known by technical audiences and able to show skewness and outliers. Here's an example with 20 mostly skewed distributions of 1000 dat... | Standardizing skewed distributions for visualisation alongside others | It may help if you can provide examples of what you've got and what you're going for.
For unknown distributions, it's hard to beat box plots. Not that they're perfect, but they are well known by techn | Standardizing skewed distributions for visualisation alongside others
It may help if you can provide examples of what you've got and what you're going for.
For unknown distributions, it's hard to beat box plots. Not that they're perfect, but they are well known by technical audiences and able to show skewness and outli... | Standardizing skewed distributions for visualisation alongside others
It may help if you can provide examples of what you've got and what you're going for.
For unknown distributions, it's hard to beat box plots. Not that they're perfect, but they are well known by techn |
50,458 | How can I approximate the median with a linear function? | We are looking to find $N$ constrained $w_i$ with $\sum_{i=1}^n w_i=1$ which minimize
$$E\left[\left(\sum_{i=1}^N w_i X_i - \text{median}(X)\right)^{\!2\ }\right]$$
Equivalently, we are looking to find $N-1$ unconstrained $w_i$ which minimize
$$E\left[\left(\sum_{i=1}^{N-1} w_i(X_i-X_N) + X_N - \text{median}(X)\right)^... | How can I approximate the median with a linear function? | We are looking to find $N$ constrained $w_i$ with $\sum_{i=1}^n w_i=1$ which minimize
$$E\left[\left(\sum_{i=1}^N w_i X_i - \text{median}(X)\right)^{\!2\ }\right]$$
Equivalently, we are looking to fin | How can I approximate the median with a linear function?
We are looking to find $N$ constrained $w_i$ with $\sum_{i=1}^n w_i=1$ which minimize
$$E\left[\left(\sum_{i=1}^N w_i X_i - \text{median}(X)\right)^{\!2\ }\right]$$
Equivalently, we are looking to find $N-1$ unconstrained $w_i$ which minimize
$$E\left[\left(\sum_... | How can I approximate the median with a linear function?
We are looking to find $N$ constrained $w_i$ with $\sum_{i=1}^n w_i=1$ which minimize
$$E\left[\left(\sum_{i=1}^N w_i X_i - \text{median}(X)\right)^{\!2\ }\right]$$
Equivalently, we are looking to fin |
50,459 | How can I approximate the median with a linear function? | This is work towards an answer, too long for a comment:
One precise version of this question is: What vector of weights $w$ makes $w\cdot X$ the best estimate of the median of $X$, where $X$ is a normally distributed $n$-dimensional vector with mean $\mu$ and covariance matrix $\Sigma$?
This is different from asking fo... | How can I approximate the median with a linear function? | This is work towards an answer, too long for a comment:
One precise version of this question is: What vector of weights $w$ makes $w\cdot X$ the best estimate of the median of $X$, where $X$ is a norm | How can I approximate the median with a linear function?
This is work towards an answer, too long for a comment:
One precise version of this question is: What vector of weights $w$ makes $w\cdot X$ the best estimate of the median of $X$, where $X$ is a normally distributed $n$-dimensional vector with mean $\mu$ and cov... | How can I approximate the median with a linear function?
This is work towards an answer, too long for a comment:
One precise version of this question is: What vector of weights $w$ makes $w\cdot X$ the best estimate of the median of $X$, where $X$ is a norm |
50,460 | gbm could make prediction out of thin air? | This is due to the default argument, bag.fraction=0.5. At this setting, for each tree a random 50% of the data is used for fitting. In your code, pred is the mean response for the 50% of rows that were chosen for the 100th tree. If you set bag.fraction=1 the mean prediction equals the mean response:
bm <- gbm(y~., data... | gbm could make prediction out of thin air? | This is due to the default argument, bag.fraction=0.5. At this setting, for each tree a random 50% of the data is used for fitting. In your code, pred is the mean response for the 50% of rows that wer | gbm could make prediction out of thin air?
This is due to the default argument, bag.fraction=0.5. At this setting, for each tree a random 50% of the data is used for fitting. In your code, pred is the mean response for the 50% of rows that were chosen for the 100th tree. If you set bag.fraction=1 the mean prediction eq... | gbm could make prediction out of thin air?
This is due to the default argument, bag.fraction=0.5. At this setting, for each tree a random 50% of the data is used for fitting. In your code, pred is the mean response for the 50% of rows that wer |
50,461 | How to classify temporal disease data | I'd encourage you to start with simple classification techniques. Typically a logistic regression. Very simple, runs fast, many examples in R online.
A simple decision tree is also an option (don't go for random forests if you don't have to).
Start with these, as they are simple to understand and have good implementa... | How to classify temporal disease data | I'd encourage you to start with simple classification techniques. Typically a logistic regression. Very simple, runs fast, many examples in R online.
A simple decision tree is also an option (don't g | How to classify temporal disease data
I'd encourage you to start with simple classification techniques. Typically a logistic regression. Very simple, runs fast, many examples in R online.
A simple decision tree is also an option (don't go for random forests if you don't have to).
Start with these, as they are simple ... | How to classify temporal disease data
I'd encourage you to start with simple classification techniques. Typically a logistic regression. Very simple, runs fast, many examples in R online.
A simple decision tree is also an option (don't g |
50,462 | How to classify temporal disease data | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
Python scikit-learn allows for analysis like the one y... | How to classify temporal disease data | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
| How to classify temporal disease data
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
Python scikit-le... | How to classify temporal disease data
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
|
50,463 | Looking for a stochastic curve fitting method | You can use the geom_smooth() function of the ggplot2 library in R.
Here is an example:
x = seq(0, 100, by=0.5)
y = sqrt(x)
y = y + rnorm(n = length(y),mean = 0,sd = 3)
df = data.frame(cbind(x,y))
require(ggplot2)
ggplot(data = df, aes(x = x,y = y)) + geom_smooth()
This is the output:
geom_smooth calls a curve fitti... | Looking for a stochastic curve fitting method | You can use the geom_smooth() function of the ggplot2 library in R.
Here is an example:
x = seq(0, 100, by=0.5)
y = sqrt(x)
y = y + rnorm(n = length(y),mean = 0,sd = 3)
df = data.frame(cbind(x,y))
re | Looking for a stochastic curve fitting method
You can use the geom_smooth() function of the ggplot2 library in R.
Here is an example:
x = seq(0, 100, by=0.5)
y = sqrt(x)
y = y + rnorm(n = length(y),mean = 0,sd = 3)
df = data.frame(cbind(x,y))
require(ggplot2)
ggplot(data = df, aes(x = x,y = y)) + geom_smooth()
This i... | Looking for a stochastic curve fitting method
You can use the geom_smooth() function of the ggplot2 library in R.
Here is an example:
x = seq(0, 100, by=0.5)
y = sqrt(x)
y = y + rnorm(n = length(y),mean = 0,sd = 3)
df = data.frame(cbind(x,y))
re |
50,464 | Variance-Covariance Matrix for $l_1$ regularized binomial logistic regression | (This answer is more of a comment than a full answer, but I'm posting it here since I don't have enough rep to comment.)
This is a very hard question to give an good answer to. Even in the non-penalized case, the covariance estimate for the parameters is based on a normal approximation. When you start penalizing, you a... | Variance-Covariance Matrix for $l_1$ regularized binomial logistic regression | (This answer is more of a comment than a full answer, but I'm posting it here since I don't have enough rep to comment.)
This is a very hard question to give an good answer to. Even in the non-penaliz | Variance-Covariance Matrix for $l_1$ regularized binomial logistic regression
(This answer is more of a comment than a full answer, but I'm posting it here since I don't have enough rep to comment.)
This is a very hard question to give an good answer to. Even in the non-penalized case, the covariance estimate for the p... | Variance-Covariance Matrix for $l_1$ regularized binomial logistic regression
(This answer is more of a comment than a full answer, but I'm posting it here since I don't have enough rep to comment.)
This is a very hard question to give an good answer to. Even in the non-penaliz |
50,465 | Interpreting QLIKE and MSE Loss function (Patton 2011) | Just noticed this question is over two years old, but this answer might prove useful for future readers.
Check Figure 3 of the paper you referenced in the comments, or Figure 1 of the version of the paper that was published in the JoE. This figure demonstrates the shape of several different loss functions, including bo... | Interpreting QLIKE and MSE Loss function (Patton 2011) | Just noticed this question is over two years old, but this answer might prove useful for future readers.
Check Figure 3 of the paper you referenced in the comments, or Figure 1 of the version of the p | Interpreting QLIKE and MSE Loss function (Patton 2011)
Just noticed this question is over two years old, but this answer might prove useful for future readers.
Check Figure 3 of the paper you referenced in the comments, or Figure 1 of the version of the paper that was published in the JoE. This figure demonstrates the ... | Interpreting QLIKE and MSE Loss function (Patton 2011)
Just noticed this question is over two years old, but this answer might prove useful for future readers.
Check Figure 3 of the paper you referenced in the comments, or Figure 1 of the version of the p |
50,466 | How to make sense of non-linear data transformations? What conclusions drawn can you apply to original data? | This question is a similar to: Interpretation of log transformed predictor. I recommend looking at the answer by jthetzel (profile: https://stats.stackexchange.com/users/2981/jthetzel) who summarized the effects of multiple well known transformations and their meanings (and posted great links).
It should be noted that ... | How to make sense of non-linear data transformations? What conclusions drawn can you apply to origin | This question is a similar to: Interpretation of log transformed predictor. I recommend looking at the answer by jthetzel (profile: https://stats.stackexchange.com/users/2981/jthetzel) who summarized | How to make sense of non-linear data transformations? What conclusions drawn can you apply to original data?
This question is a similar to: Interpretation of log transformed predictor. I recommend looking at the answer by jthetzel (profile: https://stats.stackexchange.com/users/2981/jthetzel) who summarized the effects... | How to make sense of non-linear data transformations? What conclusions drawn can you apply to origin
This question is a similar to: Interpretation of log transformed predictor. I recommend looking at the answer by jthetzel (profile: https://stats.stackexchange.com/users/2981/jthetzel) who summarized |
50,467 | Sensitivity Analysis for Missing Not at Random (MNAR) data | I'm currently dealing with that same problem too.
I have a data set with 70 kovariables and a lot of them have missing values. Most of them are definitely MNAR.
One great paper i found is this one.
http://journals.lww.com/epidem/Fulltext/2011/03000/Sensitivity_Analysis_When_Data_Are_Missing.25.aspx
they also perform a ... | Sensitivity Analysis for Missing Not at Random (MNAR) data | I'm currently dealing with that same problem too.
I have a data set with 70 kovariables and a lot of them have missing values. Most of them are definitely MNAR.
One great paper i found is this one.
ht | Sensitivity Analysis for Missing Not at Random (MNAR) data
I'm currently dealing with that same problem too.
I have a data set with 70 kovariables and a lot of them have missing values. Most of them are definitely MNAR.
One great paper i found is this one.
http://journals.lww.com/epidem/Fulltext/2011/03000/Sensitivity_... | Sensitivity Analysis for Missing Not at Random (MNAR) data
I'm currently dealing with that same problem too.
I have a data set with 70 kovariables and a lot of them have missing values. Most of them are definitely MNAR.
One great paper i found is this one.
ht |
50,468 | Help Deriving Variance Function - Binomial GLM | I think I figured it out. It's important to mention that the discussion in Faraway was in the context of IRWLS.
First of all, we can use either the variance of the response or the variance function in our IRWLS implementation. It just represents a scale change: $Var(Y)=V(\mu)a(\phi)$ where $a(\phi)$ is just some con... | Help Deriving Variance Function - Binomial GLM | I think I figured it out. It's important to mention that the discussion in Faraway was in the context of IRWLS.
First of all, we can use either the variance of the response or the variance function | Help Deriving Variance Function - Binomial GLM
I think I figured it out. It's important to mention that the discussion in Faraway was in the context of IRWLS.
First of all, we can use either the variance of the response or the variance function in our IRWLS implementation. It just represents a scale change: $Var(Y)=... | Help Deriving Variance Function - Binomial GLM
I think I figured it out. It's important to mention that the discussion in Faraway was in the context of IRWLS.
First of all, we can use either the variance of the response or the variance function |
50,469 | Help Deriving Variance Function - Binomial GLM | There is nothing wrong in your derivation, however, the variance function for exponential family written in your form is
$$V(\mu)=a(\phi)b^{''}(\theta)$$
Which is exactly $\frac{\mu(1-\mu)}{n}$. | Help Deriving Variance Function - Binomial GLM | There is nothing wrong in your derivation, however, the variance function for exponential family written in your form is
$$V(\mu)=a(\phi)b^{''}(\theta)$$
Which is exactly $\frac{\mu(1-\mu)}{n}$. | Help Deriving Variance Function - Binomial GLM
There is nothing wrong in your derivation, however, the variance function for exponential family written in your form is
$$V(\mu)=a(\phi)b^{''}(\theta)$$
Which is exactly $\frac{\mu(1-\mu)}{n}$. | Help Deriving Variance Function - Binomial GLM
There is nothing wrong in your derivation, however, the variance function for exponential family written in your form is
$$V(\mu)=a(\phi)b^{''}(\theta)$$
Which is exactly $\frac{\mu(1-\mu)}{n}$. |
50,470 | Does JAGS have an R front end like brms for Stan? [closed] | Based on your last comment ("I'm hoping for a runtime translation of R-formula syntax into JAGS model specification"), I think runjags::template.jags does what you want (at least partly). It automatically generates a complete JAGS model (and data) representation of a (G)L(M)M based on lme4-style syntax and a data fram... | Does JAGS have an R front end like brms for Stan? [closed] | Based on your last comment ("I'm hoping for a runtime translation of R-formula syntax into JAGS model specification"), I think runjags::template.jags does what you want (at least partly). It automati | Does JAGS have an R front end like brms for Stan? [closed]
Based on your last comment ("I'm hoping for a runtime translation of R-formula syntax into JAGS model specification"), I think runjags::template.jags does what you want (at least partly). It automatically generates a complete JAGS model (and data) representati... | Does JAGS have an R front end like brms for Stan? [closed]
Based on your last comment ("I'm hoping for a runtime translation of R-formula syntax into JAGS model specification"), I think runjags::template.jags does what you want (at least partly). It automati |
50,471 | Hausman Test interpretation is based on the p-value? - R output | Yes. See the following taken from a Princeton slide:
To decide between fixed or random effects you can run a Hausman test
where the null hypothesis is that the preferred model is random
effects vs. the alternative the fixed effects (see Green, 2008,
chapter 9). It basically tests whether the unique errors (ui) a... | Hausman Test interpretation is based on the p-value? - R output | Yes. See the following taken from a Princeton slide:
To decide between fixed or random effects you can run a Hausman test
where the null hypothesis is that the preferred model is random
effects v | Hausman Test interpretation is based on the p-value? - R output
Yes. See the following taken from a Princeton slide:
To decide between fixed or random effects you can run a Hausman test
where the null hypothesis is that the preferred model is random
effects vs. the alternative the fixed effects (see Green, 2008,
... | Hausman Test interpretation is based on the p-value? - R output
Yes. See the following taken from a Princeton slide:
To decide between fixed or random effects you can run a Hausman test
where the null hypothesis is that the preferred model is random
effects v |
50,472 | Why neural and convolutional neural network detect edges first? | Convolution operation have close relationship to the frequency domain. See Convolution Theorem for details.
What makes edge an edge? Sudden changes / high frequency changes on the value. Intuitively this is why convolution can detect edges.
For example, Think about the following 1D toy data.
000000000000111111111111... | Why neural and convolutional neural network detect edges first? | Convolution operation have close relationship to the frequency domain. See Convolution Theorem for details.
What makes edge an edge? Sudden changes / high frequency changes on the value. Intuitively t | Why neural and convolutional neural network detect edges first?
Convolution operation have close relationship to the frequency domain. See Convolution Theorem for details.
What makes edge an edge? Sudden changes / high frequency changes on the value. Intuitively this is why convolution can detect edges.
For example, ... | Why neural and convolutional neural network detect edges first?
Convolution operation have close relationship to the frequency domain. See Convolution Theorem for details.
What makes edge an edge? Sudden changes / high frequency changes on the value. Intuitively t |
50,473 | Why neural and convolutional neural network detect edges first? | One thing that makes neural networks so interesting is that every subset of layers can be thought of as a neural network itself. So, after the first layer transforms its input, the second-through-last layers can be thought of as a network of their own.
So, in an optimized network, the goal of the first layer is to tran... | Why neural and convolutional neural network detect edges first? | One thing that makes neural networks so interesting is that every subset of layers can be thought of as a neural network itself. So, after the first layer transforms its input, the second-through-last | Why neural and convolutional neural network detect edges first?
One thing that makes neural networks so interesting is that every subset of layers can be thought of as a neural network itself. So, after the first layer transforms its input, the second-through-last layers can be thought of as a network of their own.
So,... | Why neural and convolutional neural network detect edges first?
One thing that makes neural networks so interesting is that every subset of layers can be thought of as a neural network itself. So, after the first layer transforms its input, the second-through-last |
50,474 | Guidelines to improve a convolutional neural network? | Would anybody be aware of how to tackle the problem of finding good parameters (including architecture) for a CNN apart from trying?
No. Hence, they are optimized by 'graduate student descent' :)
A standard-ish architecture for mnist is lenet-5, and closely related variants, eg Karpathy's convnetjs implementation, whi... | Guidelines to improve a convolutional neural network? | Would anybody be aware of how to tackle the problem of finding good parameters (including architecture) for a CNN apart from trying?
No. Hence, they are optimized by 'graduate student descent' :)
A s | Guidelines to improve a convolutional neural network?
Would anybody be aware of how to tackle the problem of finding good parameters (including architecture) for a CNN apart from trying?
No. Hence, they are optimized by 'graduate student descent' :)
A standard-ish architecture for mnist is lenet-5, and closely related... | Guidelines to improve a convolutional neural network?
Would anybody be aware of how to tackle the problem of finding good parameters (including architecture) for a CNN apart from trying?
No. Hence, they are optimized by 'graduate student descent' :)
A s |
50,475 | Guidelines to improve a convolutional neural network? | You might decorrelate your data first using PCA, and then clamp the objects to your input nodes (i.e., input the PCs from PCA into the CNN). Did you select any features, or use everything? (don't know if the features were pre-selected and users are expected to use everything?). | Guidelines to improve a convolutional neural network? | You might decorrelate your data first using PCA, and then clamp the objects to your input nodes (i.e., input the PCs from PCA into the CNN). Did you select any features, or use everything? (don't kn | Guidelines to improve a convolutional neural network?
You might decorrelate your data first using PCA, and then clamp the objects to your input nodes (i.e., input the PCs from PCA into the CNN). Did you select any features, or use everything? (don't know if the features were pre-selected and users are expected to use... | Guidelines to improve a convolutional neural network?
You might decorrelate your data first using PCA, and then clamp the objects to your input nodes (i.e., input the PCs from PCA into the CNN). Did you select any features, or use everything? (don't kn |
50,476 | Guidelines to improve a convolutional neural network? | I would suggest you try another data set. MNIST data has been "over tuned" on test data set!!
You can try to run a test for "human accuracy" on this data, and you may not get over 95% accuracy. BTW, I tried, there are many digits are not quite recognizable. Here is an example, it can be 3 or 5.
In sum, today's NN tool... | Guidelines to improve a convolutional neural network? | I would suggest you try another data set. MNIST data has been "over tuned" on test data set!!
You can try to run a test for "human accuracy" on this data, and you may not get over 95% accuracy. BTW, I | Guidelines to improve a convolutional neural network?
I would suggest you try another data set. MNIST data has been "over tuned" on test data set!!
You can try to run a test for "human accuracy" on this data, and you may not get over 95% accuracy. BTW, I tried, there are many digits are not quite recognizable. Here is ... | Guidelines to improve a convolutional neural network?
I would suggest you try another data set. MNIST data has been "over tuned" on test data set!!
You can try to run a test for "human accuracy" on this data, and you may not get over 95% accuracy. BTW, I |
50,477 | How do I test difference in Percentages? | Use a hypergeometric test to see whether each region's proportion is significantly greater or significantly less than the national proportion.
The hypergeometric test treats the population as a bag with 21568 stones, 10820 of which are white. Each region is then treated as a random sample from that bag. For example th... | How do I test difference in Percentages? | Use a hypergeometric test to see whether each region's proportion is significantly greater or significantly less than the national proportion.
The hypergeometric test treats the population as a bag w | How do I test difference in Percentages?
Use a hypergeometric test to see whether each region's proportion is significantly greater or significantly less than the national proportion.
The hypergeometric test treats the population as a bag with 21568 stones, 10820 of which are white. Each region is then treated as a ra... | How do I test difference in Percentages?
Use a hypergeometric test to see whether each region's proportion is significantly greater or significantly less than the national proportion.
The hypergeometric test treats the population as a bag w |
50,478 | PCA vs FA vs ICA for dimensionality reduction in questionaire data | I was curious about your question, because I had never even heard of Independent Component Analysis (ICA), but I use factor analysis all the time. So looking up ICA, I found that one of the key assumptions was that "the values in each source signal have non-Gaussian distributions" (Wikipedia). This doesn't seem like a ... | PCA vs FA vs ICA for dimensionality reduction in questionaire data | I was curious about your question, because I had never even heard of Independent Component Analysis (ICA), but I use factor analysis all the time. So looking up ICA, I found that one of the key assump | PCA vs FA vs ICA for dimensionality reduction in questionaire data
I was curious about your question, because I had never even heard of Independent Component Analysis (ICA), but I use factor analysis all the time. So looking up ICA, I found that one of the key assumptions was that "the values in each source signal have... | PCA vs FA vs ICA for dimensionality reduction in questionaire data
I was curious about your question, because I had never even heard of Independent Component Analysis (ICA), but I use factor analysis all the time. So looking up ICA, I found that one of the key assump |
50,479 | Critical region of likelihood ratio test | $\chi^2_1(0.95) = 3.841$
$C = \{\mathbf{Y}: (Y_1+3Y_2) \log{\frac{p_0}{\hat{p}}} + (Y_1+3Y_0) \log(\frac{1-p_0}{1-\hat{p}}) \geq \frac{-\chi^2_1(0.95)}{2} \}$
When $Y_0 =Y_2$, $\hat{p} = 1/2$
Thus, $\log(4p_0(1-p_0)) \geq \frac{-3.841}{2(Y_1+3Y_2)} $ $\implies p_0(1-p_0) \geq 0.25 \exp{(\frac{-1.92}{Y_1+3Y_2})}$ | Critical region of likelihood ratio test | $\chi^2_1(0.95) = 3.841$
$C = \{\mathbf{Y}: (Y_1+3Y_2) \log{\frac{p_0}{\hat{p}}} + (Y_1+3Y_0) \log(\frac{1-p_0}{1-\hat{p}}) \geq \frac{-\chi^2_1(0.95)}{2} \}$
When $Y_0 =Y_2$, $\hat{p} = 1/2$
Thus, $\ | Critical region of likelihood ratio test
$\chi^2_1(0.95) = 3.841$
$C = \{\mathbf{Y}: (Y_1+3Y_2) \log{\frac{p_0}{\hat{p}}} + (Y_1+3Y_0) \log(\frac{1-p_0}{1-\hat{p}}) \geq \frac{-\chi^2_1(0.95)}{2} \}$
When $Y_0 =Y_2$, $\hat{p} = 1/2$
Thus, $\log(4p_0(1-p_0)) \geq \frac{-3.841}{2(Y_1+3Y_2)} $ $\implies p_0(1-p_0) \geq 0.... | Critical region of likelihood ratio test
$\chi^2_1(0.95) = 3.841$
$C = \{\mathbf{Y}: (Y_1+3Y_2) \log{\frac{p_0}{\hat{p}}} + (Y_1+3Y_0) \log(\frac{1-p_0}{1-\hat{p}}) \geq \frac{-\chi^2_1(0.95)}{2} \}$
When $Y_0 =Y_2$, $\hat{p} = 1/2$
Thus, $\ |
50,480 | How to better plot and compare overlapping histograms? | The usua alternatives to display "overlapping" histograms are to:
place the bar side by side (but I don't think that it is working well visually in most of the situations):
connect the heights of the bars with a line (and drop the bar itself - there exists alternatives where the outline of the histogram is plotted,... | How to better plot and compare overlapping histograms? | The usua alternatives to display "overlapping" histograms are to:
place the bar side by side (but I don't think that it is working well visually in most of the situations):
connect the heights of | How to better plot and compare overlapping histograms?
The usua alternatives to display "overlapping" histograms are to:
place the bar side by side (but I don't think that it is working well visually in most of the situations):
connect the heights of the bars with a line (and drop the bar itself - there exists alte... | How to better plot and compare overlapping histograms?
The usua alternatives to display "overlapping" histograms are to:
place the bar side by side (but I don't think that it is working well visually in most of the situations):
connect the heights of |
50,481 | How to better plot and compare overlapping histograms? | Plotting histograms together can be fine, but it breaks down when you have more than two histograms, or the more they overlap, both of which apply in your case. I would suggest you start by making a plot matrix (so long as you don't have so many groups the plots become unusable).
Likewise, plots with too many, and t... | How to better plot and compare overlapping histograms? | Plotting histograms together can be fine, but it breaks down when you have more than two histograms, or the more they overlap, both of which apply in your case. I would suggest you start by making a | How to better plot and compare overlapping histograms?
Plotting histograms together can be fine, but it breaks down when you have more than two histograms, or the more they overlap, both of which apply in your case. I would suggest you start by making a plot matrix (so long as you don't have so many groups the plots b... | How to better plot and compare overlapping histograms?
Plotting histograms together can be fine, but it breaks down when you have more than two histograms, or the more they overlap, both of which apply in your case. I would suggest you start by making a |
50,482 | Reproducible benchmarks for the performance of statistical prediction methods? | Tentative answer here but, well, there's a paper [1] comparing the performance of 22 classification algorithms predicting software failures in 10 public domain NASA Metrics Data repository datasets.
The data used in this study stems from the NASA MDP repository [10]. Ten software defect prediction data sets are analyze... | Reproducible benchmarks for the performance of statistical prediction methods? | Tentative answer here but, well, there's a paper [1] comparing the performance of 22 classification algorithms predicting software failures in 10 public domain NASA Metrics Data repository datasets.
T | Reproducible benchmarks for the performance of statistical prediction methods?
Tentative answer here but, well, there's a paper [1] comparing the performance of 22 classification algorithms predicting software failures in 10 public domain NASA Metrics Data repository datasets.
The data used in this study stems from the... | Reproducible benchmarks for the performance of statistical prediction methods?
Tentative answer here but, well, there's a paper [1] comparing the performance of 22 classification algorithms predicting software failures in 10 public domain NASA Metrics Data repository datasets.
T |
50,483 | Test goodness of fit for geometric distribution | The differences between the "events" have a poisson distribution. Let $N(l)$ be the number of "events" to occur in $[0,l]$, $l$ fixed.
We know,
$$P\{N(l) = k\} = \frac{e^{-\lambda l}(\lambda l)^k}{k!}, k=0,1,2,...$$
take $0<l_1<l_2<...<l_n<\infty$ where any given difference
$$N(l_1), N(l_2)-N(l_1), ..., N(l_n)-N(l_{... | Test goodness of fit for geometric distribution | The differences between the "events" have a poisson distribution. Let $N(l)$ be the number of "events" to occur in $[0,l]$, $l$ fixed.
We know,
$$P\{N(l) = k\} = \frac{e^{-\lambda l}(\lambda l)^k}{k | Test goodness of fit for geometric distribution
The differences between the "events" have a poisson distribution. Let $N(l)$ be the number of "events" to occur in $[0,l]$, $l$ fixed.
We know,
$$P\{N(l) = k\} = \frac{e^{-\lambda l}(\lambda l)^k}{k!}, k=0,1,2,...$$
take $0<l_1<l_2<...<l_n<\infty$ where any given differ... | Test goodness of fit for geometric distribution
The differences between the "events" have a poisson distribution. Let $N(l)$ be the number of "events" to occur in $[0,l]$, $l$ fixed.
We know,
$$P\{N(l) = k\} = \frac{e^{-\lambda l}(\lambda l)^k}{k |
50,484 | Test goodness of fit for geometric distribution | I have a very similar problem to yours actually, also non-overlapping variable length intervals.
I am right now working on this so I cant give a full answer, also I am not a statistician so I wont even approach this from a math side.
But for your 2nd question, one possible approach is the following:
you compute the in... | Test goodness of fit for geometric distribution | I have a very similar problem to yours actually, also non-overlapping variable length intervals.
I am right now working on this so I cant give a full answer, also I am not a statistician so I wont eve | Test goodness of fit for geometric distribution
I have a very similar problem to yours actually, also non-overlapping variable length intervals.
I am right now working on this so I cant give a full answer, also I am not a statistician so I wont even approach this from a math side.
But for your 2nd question, one possibl... | Test goodness of fit for geometric distribution
I have a very similar problem to yours actually, also non-overlapping variable length intervals.
I am right now working on this so I cant give a full answer, also I am not a statistician so I wont eve |
50,485 | Sigmoid type functions for logistic regression | Not all distributions' CDFs are sigmoid. Consider the CDF of the uniform distribution (figure copied from Wikipedia):
Other distributions may be less obvious, but still problematic. Consider the CDF of a Gamma distribution with $k=.5,\ \theta=1$ (the lavender line at the far left; figure copied from Wikipedia):
... | Sigmoid type functions for logistic regression | Not all distributions' CDFs are sigmoid. Consider the CDF of the uniform distribution (figure copied from Wikipedia):
Other distributions may be less obvious, but still problematic. Consider the | Sigmoid type functions for logistic regression
Not all distributions' CDFs are sigmoid. Consider the CDF of the uniform distribution (figure copied from Wikipedia):
Other distributions may be less obvious, but still problematic. Consider the CDF of a Gamma distribution with $k=.5,\ \theta=1$ (the lavender line at ... | Sigmoid type functions for logistic regression
Not all distributions' CDFs are sigmoid. Consider the CDF of the uniform distribution (figure copied from Wikipedia):
Other distributions may be less obvious, but still problematic. Consider the |
50,486 | Sigmoid type functions for logistic regression | I believe tanh(z) is a good replacement for the sigmoid function. Behaves almost exactly the same way. Also, the gradient descent update expression is the same | Sigmoid type functions for logistic regression | I believe tanh(z) is a good replacement for the sigmoid function. Behaves almost exactly the same way. Also, the gradient descent update expression is the same | Sigmoid type functions for logistic regression
I believe tanh(z) is a good replacement for the sigmoid function. Behaves almost exactly the same way. Also, the gradient descent update expression is the same | Sigmoid type functions for logistic regression
I believe tanh(z) is a good replacement for the sigmoid function. Behaves almost exactly the same way. Also, the gradient descent update expression is the same |
50,487 | Show that the signal $x_n = A \cos(\omega n)$ can be fully predicted by a system with two weights $w_1,w_2$ | This is basically equivalent to, given $\cos(a)$, $\cos(a-\omega)$, predict $\cos(a+\omega)$.
$$\cos(a-\omega)=\cos(a)\cos(\omega)+\sin(a)\sin(\omega)$$
$$\cos(a+\omega)=\cos(a)\cos(\omega)-\sin(a)\sin(\omega)$$
So let $w_1=2\cos(\omega)$, $w_2=-1$, we have
$$w_1\cos(a)+w_2\cos(a-\omega)=2\cos(\omega)\cos(a)-\cos(a)\co... | Show that the signal $x_n = A \cos(\omega n)$ can be fully predicted by a system with two weights $w | This is basically equivalent to, given $\cos(a)$, $\cos(a-\omega)$, predict $\cos(a+\omega)$.
$$\cos(a-\omega)=\cos(a)\cos(\omega)+\sin(a)\sin(\omega)$$
$$\cos(a+\omega)=\cos(a)\cos(\omega)-\sin(a)\si | Show that the signal $x_n = A \cos(\omega n)$ can be fully predicted by a system with two weights $w_1,w_2$
This is basically equivalent to, given $\cos(a)$, $\cos(a-\omega)$, predict $\cos(a+\omega)$.
$$\cos(a-\omega)=\cos(a)\cos(\omega)+\sin(a)\sin(\omega)$$
$$\cos(a+\omega)=\cos(a)\cos(\omega)-\sin(a)\sin(\omega)$$
... | Show that the signal $x_n = A \cos(\omega n)$ can be fully predicted by a system with two weights $w
This is basically equivalent to, given $\cos(a)$, $\cos(a-\omega)$, predict $\cos(a+\omega)$.
$$\cos(a-\omega)=\cos(a)\cos(\omega)+\sin(a)\sin(\omega)$$
$$\cos(a+\omega)=\cos(a)\cos(\omega)-\sin(a)\si |
50,488 | Can different classification methods be compared in the same manner as models during hyper-parameter tuning? | Yes, you can generalize the procedure for selecting from very different models. Think of optimizing the "training algorithm" hyperparameter. | Can different classification methods be compared in the same manner as models during hyper-parameter | Yes, you can generalize the procedure for selecting from very different models. Think of optimizing the "training algorithm" hyperparameter. | Can different classification methods be compared in the same manner as models during hyper-parameter tuning?
Yes, you can generalize the procedure for selecting from very different models. Think of optimizing the "training algorithm" hyperparameter. | Can different classification methods be compared in the same manner as models during hyper-parameter
Yes, you can generalize the procedure for selecting from very different models. Think of optimizing the "training algorithm" hyperparameter. |
50,489 | Selecting Link Function for Negative Binomial GLM | First, you need to understand better what link functions are. Then, maybe look at what others are doing in your field, for instance this paper.
Then, you have count data, and for such data the most natural link function is the log link function. See for example Goodness of fit and which model to choose linear regressi... | Selecting Link Function for Negative Binomial GLM | First, you need to understand better what link functions are. Then, maybe look at what others are doing in your field, for instance this paper.
Then, you have count data, and for such data the most n | Selecting Link Function for Negative Binomial GLM
First, you need to understand better what link functions are. Then, maybe look at what others are doing in your field, for instance this paper.
Then, you have count data, and for such data the most natural link function is the log link function. See for example Goodnes... | Selecting Link Function for Negative Binomial GLM
First, you need to understand better what link functions are. Then, maybe look at what others are doing in your field, for instance this paper.
Then, you have count data, and for such data the most n |
50,490 | What's the "best" way to calculate sample size for A/B tests? | There is no best to use because each method relates to specific assumptions about the testing methodology. Evan Miller's calculator calculates sample size for a two-tailed test. In the past Optimizely's calculator was calculating samples for a one-tailed test. Currently, Optimizely uses a Bayesian states engine and t... | What's the "best" way to calculate sample size for A/B tests? | There is no best to use because each method relates to specific assumptions about the testing methodology. Evan Miller's calculator calculates sample size for a two-tailed test. In the past Optimize | What's the "best" way to calculate sample size for A/B tests?
There is no best to use because each method relates to specific assumptions about the testing methodology. Evan Miller's calculator calculates sample size for a two-tailed test. In the past Optimizely's calculator was calculating samples for a one-tailed t... | What's the "best" way to calculate sample size for A/B tests?
There is no best to use because each method relates to specific assumptions about the testing methodology. Evan Miller's calculator calculates sample size for a two-tailed test. In the past Optimize |
50,491 | Comparing two datasets with same variable | If you don't have concern about the accuraccy degrading over time or don't have concerns that the time of day results in less accurate measurements then I would advocate simplicity here through the use of a paired-sample t-test. You have completely missing data for the :15 and :45 intervals, so I'd throw those measure... | Comparing two datasets with same variable | If you don't have concern about the accuraccy degrading over time or don't have concerns that the time of day results in less accurate measurements then I would advocate simplicity here through the us | Comparing two datasets with same variable
If you don't have concern about the accuraccy degrading over time or don't have concerns that the time of day results in less accurate measurements then I would advocate simplicity here through the use of a paired-sample t-test. You have completely missing data for the :15 and... | Comparing two datasets with same variable
If you don't have concern about the accuraccy degrading over time or don't have concerns that the time of day results in less accurate measurements then I would advocate simplicity here through the us |
50,492 | Aging data in the German tank problem | A reasonable approach may be to estimate the production rate by always using the maximum time period available. That is, create an estimate of $N$ every day, but use today's estimate along with the day 1 estimate and the number of days that have passed to get the estimated production rate.
For $i \ge 2,$ your day $i$ ... | Aging data in the German tank problem | A reasonable approach may be to estimate the production rate by always using the maximum time period available. That is, create an estimate of $N$ every day, but use today's estimate along with the da | Aging data in the German tank problem
A reasonable approach may be to estimate the production rate by always using the maximum time period available. That is, create an estimate of $N$ every day, but use today's estimate along with the day 1 estimate and the number of days that have passed to get the estimated producti... | Aging data in the German tank problem
A reasonable approach may be to estimate the production rate by always using the maximum time period available. That is, create an estimate of $N$ every day, but use today's estimate along with the da |
50,493 | DBSCAN: What is a Core Point? | In a database, all points are equal.
The blue point has 1 point in its neighborhood - itself.
The yellow points have 2 points in their neighborhood each.
The red points have 4-5 points in their neighborhood each.
Note that the definitions don't say "minPts other points"; but "minPts points". You can't ignore the one ... | DBSCAN: What is a Core Point? | In a database, all points are equal.
The blue point has 1 point in its neighborhood - itself.
The yellow points have 2 points in their neighborhood each.
The red points have 4-5 points in their neigh | DBSCAN: What is a Core Point?
In a database, all points are equal.
The blue point has 1 point in its neighborhood - itself.
The yellow points have 2 points in their neighborhood each.
The red points have 4-5 points in their neighborhood each.
Note that the definitions don't say "minPts other points"; but "minPts poin... | DBSCAN: What is a Core Point?
In a database, all points are equal.
The blue point has 1 point in its neighborhood - itself.
The yellow points have 2 points in their neighborhood each.
The red points have 4-5 points in their neigh |
50,494 | What can go wrong using lagged terms as instrumental variables? | Consider a causal $ARMA(1,2)$ process
$$
Y_t=\phi Y_{t-1}+\epsilon_t+\theta_1\epsilon_{t-1}+\theta_2\epsilon_{t-2}
$$
Suppose our interest centers on estimating $\phi$, but we are not aware of the $MA$ components (or we just do not know how to fit ARMA models :-)).
One strategy might therefore consist of just running a... | What can go wrong using lagged terms as instrumental variables? | Consider a causal $ARMA(1,2)$ process
$$
Y_t=\phi Y_{t-1}+\epsilon_t+\theta_1\epsilon_{t-1}+\theta_2\epsilon_{t-2}
$$
Suppose our interest centers on estimating $\phi$, but we are not aware of the $MA | What can go wrong using lagged terms as instrumental variables?
Consider a causal $ARMA(1,2)$ process
$$
Y_t=\phi Y_{t-1}+\epsilon_t+\theta_1\epsilon_{t-1}+\theta_2\epsilon_{t-2}
$$
Suppose our interest centers on estimating $\phi$, but we are not aware of the $MA$ components (or we just do not know how to fit ARMA mod... | What can go wrong using lagged terms as instrumental variables?
Consider a causal $ARMA(1,2)$ process
$$
Y_t=\phi Y_{t-1}+\epsilon_t+\theta_1\epsilon_{t-1}+\theta_2\epsilon_{t-2}
$$
Suppose our interest centers on estimating $\phi$, but we are not aware of the $MA |
50,495 | Analysing rank-ordered data using mlogit | My experience is still rather limited with mlogit package, but if I read Croissant vignette correctly (see the beginning of sec. 1.2 Model description, page 7), the alt variable in your model is specified as alternative specific with a generic coefficient and NOT as an individual specific covariate---those variables ar... | Analysing rank-ordered data using mlogit | My experience is still rather limited with mlogit package, but if I read Croissant vignette correctly (see the beginning of sec. 1.2 Model description, page 7), the alt variable in your model is speci | Analysing rank-ordered data using mlogit
My experience is still rather limited with mlogit package, but if I read Croissant vignette correctly (see the beginning of sec. 1.2 Model description, page 7), the alt variable in your model is specified as alternative specific with a generic coefficient and NOT as an individua... | Analysing rank-ordered data using mlogit
My experience is still rather limited with mlogit package, but if I read Croissant vignette correctly (see the beginning of sec. 1.2 Model description, page 7), the alt variable in your model is speci |
50,496 | Analysing rank-ordered data using mlogit | I used the model format you mentioned at the end and was able to reproduce the LRS listed for example 8.1 in the J Marden text (Analyzing and Modeling Rank Data):
> exData = t(matrix(c(c(1,2,4,3,5), c(2,1,4,3,5), c(2,3,5,4,1), rep(c(2,1,5,3,4), 3)), nrow=5))
> exData
[,1] [,2] [,3] [,4] [,5]
[1,] 1 2 4 ... | Analysing rank-ordered data using mlogit | I used the model format you mentioned at the end and was able to reproduce the LRS listed for example 8.1 in the J Marden text (Analyzing and Modeling Rank Data):
> exData = t(matrix(c(c(1,2,4,3,5), c | Analysing rank-ordered data using mlogit
I used the model format you mentioned at the end and was able to reproduce the LRS listed for example 8.1 in the J Marden text (Analyzing and Modeling Rank Data):
> exData = t(matrix(c(c(1,2,4,3,5), c(2,1,4,3,5), c(2,3,5,4,1), rep(c(2,1,5,3,4), 3)), nrow=5))
> exData
[,1] [... | Analysing rank-ordered data using mlogit
I used the model format you mentioned at the end and was able to reproduce the LRS listed for example 8.1 in the J Marden text (Analyzing and Modeling Rank Data):
> exData = t(matrix(c(c(1,2,4,3,5), c |
50,497 | Use of Random Forests for variable importance as preprocess before another analysis | Note: this answer is incomplete.
Toy Problem:
This is a trivial problem that is typically small in dimension and as accessible as possible to human intuition and learning. Personally, I find this (link, link) demo to be accessible for my intuition and learning. So do the folks at the Max Planck Institute for Biologic... | Use of Random Forests for variable importance as preprocess before another analysis | Note: this answer is incomplete.
Toy Problem:
This is a trivial problem that is typically small in dimension and as accessible as possible to human intuition and learning. Personally, I find this (li | Use of Random Forests for variable importance as preprocess before another analysis
Note: this answer is incomplete.
Toy Problem:
This is a trivial problem that is typically small in dimension and as accessible as possible to human intuition and learning. Personally, I find this (link, link) demo to be accessible for ... | Use of Random Forests for variable importance as preprocess before another analysis
Note: this answer is incomplete.
Toy Problem:
This is a trivial problem that is typically small in dimension and as accessible as possible to human intuition and learning. Personally, I find this (li |
50,498 | Which Machine Learning book to choose (APM, MLAP or ISL)? | Opinions about a book is always subjective. I personally liked both books
Applied Predictive Modeling by Kuhn and Johnson
An Introduction to Statistical Learning by Hastie (ISL)
I like ISL better since it explains more statistic knowledge than applications. In addition, the PLUS version ESL is one of the best books i... | Which Machine Learning book to choose (APM, MLAP or ISL)? | Opinions about a book is always subjective. I personally liked both books
Applied Predictive Modeling by Kuhn and Johnson
An Introduction to Statistical Learning by Hastie (ISL)
I like ISL better si | Which Machine Learning book to choose (APM, MLAP or ISL)?
Opinions about a book is always subjective. I personally liked both books
Applied Predictive Modeling by Kuhn and Johnson
An Introduction to Statistical Learning by Hastie (ISL)
I like ISL better since it explains more statistic knowledge than applications. In... | Which Machine Learning book to choose (APM, MLAP or ISL)?
Opinions about a book is always subjective. I personally liked both books
Applied Predictive Modeling by Kuhn and Johnson
An Introduction to Statistical Learning by Hastie (ISL)
I like ISL better si |
50,499 | What is so mysterious about machine learning? | E. g. neural networks function exactly like black boxes. We know general grounds on which they work and how to train them. But we don't know what features does neural network compute on hidden layers. Well, we can guess and test our guesses. But there is no guarantee that we well succeed in our guessing or that discove... | What is so mysterious about machine learning? | E. g. neural networks function exactly like black boxes. We know general grounds on which they work and how to train them. But we don't know what features does neural network compute on hidden layers. | What is so mysterious about machine learning?
E. g. neural networks function exactly like black boxes. We know general grounds on which they work and how to train them. But we don't know what features does neural network compute on hidden layers. Well, we can guess and test our guesses. But there is no guarantee that w... | What is so mysterious about machine learning?
E. g. neural networks function exactly like black boxes. We know general grounds on which they work and how to train them. But we don't know what features does neural network compute on hidden layers. |
50,500 | Using Keras LSTM RNN for variable length sequence prediction | I see there was an issue filed last year about this. The author recommends zero-padding or batches of size 1:
Zero-padding
X = keras.preprocessing.sequence.pad_sequences(sequences, maxlen=100)
model.fit(X, y, batch_size=32, nb_epoch=10)
Batches of size 1
for seq, label in zip(sequences, y):
model.train(np.array([s... | Using Keras LSTM RNN for variable length sequence prediction | I see there was an issue filed last year about this. The author recommends zero-padding or batches of size 1:
Zero-padding
X = keras.preprocessing.sequence.pad_sequences(sequences, maxlen=100)
model. | Using Keras LSTM RNN for variable length sequence prediction
I see there was an issue filed last year about this. The author recommends zero-padding or batches of size 1:
Zero-padding
X = keras.preprocessing.sequence.pad_sequences(sequences, maxlen=100)
model.fit(X, y, batch_size=32, nb_epoch=10)
Batches of size 1
fo... | Using Keras LSTM RNN for variable length sequence prediction
I see there was an issue filed last year about this. The author recommends zero-padding or batches of size 1:
Zero-padding
X = keras.preprocessing.sequence.pad_sequences(sequences, maxlen=100)
model. |
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