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 |
|---|---|---|---|---|---|---|
7,101 | Are all simulation methods some form of Monte Carlo? | There are superb answer though, Feynman's landmark paper, that also touches upon what is a "simulation" from quantum perspective, really worth mentioning here: Simulating physics with computers.
are all simulation methods in essence Monte Carlo methods?
Feynman distinguishes 3 types of simulations: (1) Simulating ti... | Are all simulation methods some form of Monte Carlo? | There are superb answer though, Feynman's landmark paper, that also touches upon what is a "simulation" from quantum perspective, really worth mentioning here: Simulating physics with computers.
are | Are all simulation methods some form of Monte Carlo?
There are superb answer though, Feynman's landmark paper, that also touches upon what is a "simulation" from quantum perspective, really worth mentioning here: Simulating physics with computers.
are all simulation methods in essence Monte Carlo methods?
Feynman dis... | Are all simulation methods some form of Monte Carlo?
There are superb answer though, Feynman's landmark paper, that also touches upon what is a "simulation" from quantum perspective, really worth mentioning here: Simulating physics with computers.
are |
7,102 | Is it possible to change a hypothesis to match observed data (aka fishing expedition) and avoid an increase in Type I errors? | Certainly you can go on fishing expeditions, as long as you admit that it's a fishing expedition and treat it as such. A nicer name for such is "exploratory data analysis".
A better analogy might be shooting at a target:
You can shoot at a target and celebrate if you hit the bulls eye.
You can shoot without a target i... | Is it possible to change a hypothesis to match observed data (aka fishing expedition) and avoid an i | Certainly you can go on fishing expeditions, as long as you admit that it's a fishing expedition and treat it as such. A nicer name for such is "exploratory data analysis".
A better analogy might be | Is it possible to change a hypothesis to match observed data (aka fishing expedition) and avoid an increase in Type I errors?
Certainly you can go on fishing expeditions, as long as you admit that it's a fishing expedition and treat it as such. A nicer name for such is "exploratory data analysis".
A better analogy mig... | Is it possible to change a hypothesis to match observed data (aka fishing expedition) and avoid an i
Certainly you can go on fishing expeditions, as long as you admit that it's a fishing expedition and treat it as such. A nicer name for such is "exploratory data analysis".
A better analogy might be |
7,103 | Is it possible to change a hypothesis to match observed data (aka fishing expedition) and avoid an increase in Type I errors? | The problem with fishing expeditions is this: if you test enough hypotheses, one of them will be confirmed with a low p value. Let me give a concrete example.
Imagine you have are doing an epidemiological study. You have found 1000 patients that suffer from a rare condition. You want to know what they have in common. ... | Is it possible to change a hypothesis to match observed data (aka fishing expedition) and avoid an i | The problem with fishing expeditions is this: if you test enough hypotheses, one of them will be confirmed with a low p value. Let me give a concrete example.
Imagine you have are doing an epidemiolo | Is it possible to change a hypothesis to match observed data (aka fishing expedition) and avoid an increase in Type I errors?
The problem with fishing expeditions is this: if you test enough hypotheses, one of them will be confirmed with a low p value. Let me give a concrete example.
Imagine you have are doing an epid... | Is it possible to change a hypothesis to match observed data (aka fishing expedition) and avoid an i
The problem with fishing expeditions is this: if you test enough hypotheses, one of them will be confirmed with a low p value. Let me give a concrete example.
Imagine you have are doing an epidemiolo |
7,104 | Is it possible to change a hypothesis to match observed data (aka fishing expedition) and avoid an increase in Type I errors? | The question asks if there are other problems than type I error inflation that come with fishing expeditions.
A type I error occurs when you reject the null hypothesis (typically of no effect) when it is true. A generalization, related to type I errors but not quite the same, is that even when the null is false (i.e... | Is it possible to change a hypothesis to match observed data (aka fishing expedition) and avoid an i | The question asks if there are other problems than type I error inflation that come with fishing expeditions.
A type I error occurs when you reject the null hypothesis (typically of no effect) when | Is it possible to change a hypothesis to match observed data (aka fishing expedition) and avoid an increase in Type I errors?
The question asks if there are other problems than type I error inflation that come with fishing expeditions.
A type I error occurs when you reject the null hypothesis (typically of no effect)... | Is it possible to change a hypothesis to match observed data (aka fishing expedition) and avoid an i
The question asks if there are other problems than type I error inflation that come with fishing expeditions.
A type I error occurs when you reject the null hypothesis (typically of no effect) when |
7,105 | What algorithms need feature scaling, beside from SVM? | In general, algorithms that exploit distances or similarities (e.g. in the form of scalar product) between data samples, such as k-NN and SVM, are sensitive to feature transformations.
Graphical-model based classifiers, such as Fisher LDA or Naive Bayes, as well as Decision trees and Tree-based ensemble methods (RF, XG... | What algorithms need feature scaling, beside from SVM? | In general, algorithms that exploit distances or similarities (e.g. in the form of scalar product) between data samples, such as k-NN and SVM, are sensitive to feature transformations.
Graphical-model | What algorithms need feature scaling, beside from SVM?
In general, algorithms that exploit distances or similarities (e.g. in the form of scalar product) between data samples, such as k-NN and SVM, are sensitive to feature transformations.
Graphical-model based classifiers, such as Fisher LDA or Naive Bayes, as well as... | What algorithms need feature scaling, beside from SVM?
In general, algorithms that exploit distances or similarities (e.g. in the form of scalar product) between data samples, such as k-NN and SVM, are sensitive to feature transformations.
Graphical-model |
7,106 | What algorithms need feature scaling, beside from SVM? | Here is a list I found on http://www.dataschool.io/comparing-supervised-learning-algorithms/ indicating which classifier needs feature scaling:
Full table:
In k-means clustering you also need to normalize your input.
In addition to considering whether the classifier exploits distances or similarities as Yell Bond men... | What algorithms need feature scaling, beside from SVM? | Here is a list I found on http://www.dataschool.io/comparing-supervised-learning-algorithms/ indicating which classifier needs feature scaling:
Full table:
In k-means clustering you also need to nor | What algorithms need feature scaling, beside from SVM?
Here is a list I found on http://www.dataschool.io/comparing-supervised-learning-algorithms/ indicating which classifier needs feature scaling:
Full table:
In k-means clustering you also need to normalize your input.
In addition to considering whether the classif... | What algorithms need feature scaling, beside from SVM?
Here is a list I found on http://www.dataschool.io/comparing-supervised-learning-algorithms/ indicating which classifier needs feature scaling:
Full table:
In k-means clustering you also need to nor |
7,107 | What algorithms need feature scaling, beside from SVM? | Adding to the excellent (but too short) answer by Yell Bond. Look at what happens with a linear regression model, we write it with only two predictors but the issue do not depend on that.
$$
Y_i = \beta_0 + \beta_1 x_i + \beta_2 z_i + \epsilon_i
$$
$i=1, \dots, n$. Now if you, say, center and scale the predictors t... | What algorithms need feature scaling, beside from SVM? | Adding to the excellent (but too short) answer by Yell Bond. Look at what happens with a linear regression model, we write it with only two predictors but the issue do not depend on that.
$$
Y_i = | What algorithms need feature scaling, beside from SVM?
Adding to the excellent (but too short) answer by Yell Bond. Look at what happens with a linear regression model, we write it with only two predictors but the issue do not depend on that.
$$
Y_i = \beta_0 + \beta_1 x_i + \beta_2 z_i + \epsilon_i
$$
$i=1, \dots,... | What algorithms need feature scaling, beside from SVM?
Adding to the excellent (but too short) answer by Yell Bond. Look at what happens with a linear regression model, we write it with only two predictors but the issue do not depend on that.
$$
Y_i = |
7,108 | Why does "explaining away" make intuitive sense? | Clarification and notation
if C occurs, one of P(A) or P(B) increases, but the other decreases
This isn't correct. You have (implicitly and reasonably) assumed that A is (marginally) independent of B and also that A and B are the only causes of C. This implies that A and B are indeed dependent conditional on C, th... | Why does "explaining away" make intuitive sense? | Clarification and notation
if C occurs, one of P(A) or P(B) increases, but the other decreases
This isn't correct. You have (implicitly and reasonably) assumed that A is (marginally) independent o | Why does "explaining away" make intuitive sense?
Clarification and notation
if C occurs, one of P(A) or P(B) increases, but the other decreases
This isn't correct. You have (implicitly and reasonably) assumed that A is (marginally) independent of B and also that A and B are the only causes of C. This implies that ... | Why does "explaining away" make intuitive sense?
Clarification and notation
if C occurs, one of P(A) or P(B) increases, but the other decreases
This isn't correct. You have (implicitly and reasonably) assumed that A is (marginally) independent o |
7,109 | Why does "explaining away" make intuitive sense? | I think your intuition is ok but your understanding of "explain away" reasoning is wrong.
In the article you linked to
"Explaining away" is a common pattern of reasoning in which the
confirmation of one cause of an observed or believed event reduces the
need to invoke alternative causes
(emphasis added)
This is q... | Why does "explaining away" make intuitive sense? | I think your intuition is ok but your understanding of "explain away" reasoning is wrong.
In the article you linked to
"Explaining away" is a common pattern of reasoning in which the
confirmation o | Why does "explaining away" make intuitive sense?
I think your intuition is ok but your understanding of "explain away" reasoning is wrong.
In the article you linked to
"Explaining away" is a common pattern of reasoning in which the
confirmation of one cause of an observed or believed event reduces the
need to invo... | Why does "explaining away" make intuitive sense?
I think your intuition is ok but your understanding of "explain away" reasoning is wrong.
In the article you linked to
"Explaining away" is a common pattern of reasoning in which the
confirmation o |
7,110 | Why does "explaining away" make intuitive sense? | In the absence of specific additional information that changes the conditional probability of $A$ or $B$, Bayes rule tells you
$P(A|C) = \frac{P(C|A)P(A)}{P(C)}$ and similarly for $P(B|C)$
If $\frac{P(C|A)}{P(C)}$ and $\frac{P(C|B)}{P(C)}$ are both bigger than 1 (which you'd expect if the word 'explanation' is really ... | Why does "explaining away" make intuitive sense? | In the absence of specific additional information that changes the conditional probability of $A$ or $B$, Bayes rule tells you
$P(A|C) = \frac{P(C|A)P(A)}{P(C)}$ and similarly for $P(B|C)$
If $\frac{ | Why does "explaining away" make intuitive sense?
In the absence of specific additional information that changes the conditional probability of $A$ or $B$, Bayes rule tells you
$P(A|C) = \frac{P(C|A)P(A)}{P(C)}$ and similarly for $P(B|C)$
If $\frac{P(C|A)}{P(C)}$ and $\frac{P(C|B)}{P(C)}$ are both bigger than 1 (which ... | Why does "explaining away" make intuitive sense?
In the absence of specific additional information that changes the conditional probability of $A$ or $B$, Bayes rule tells you
$P(A|C) = \frac{P(C|A)P(A)}{P(C)}$ and similarly for $P(B|C)$
If $\frac{ |
7,111 | Why does "explaining away" make intuitive sense? | You're asking for intuition. What does it mean that $A$ and $B$ are independent? It means that if I tell you that I've just seen the monster, your opinion about the occurrence or not of the earthquake doesn't change; and conversely. If you think that both $P(C\mid A)$ and $P(C\mid B)$ are high, and I tell you that the ... | Why does "explaining away" make intuitive sense? | You're asking for intuition. What does it mean that $A$ and $B$ are independent? It means that if I tell you that I've just seen the monster, your opinion about the occurrence or not of the earthquake | Why does "explaining away" make intuitive sense?
You're asking for intuition. What does it mean that $A$ and $B$ are independent? It means that if I tell you that I've just seen the monster, your opinion about the occurrence or not of the earthquake doesn't change; and conversely. If you think that both $P(C\mid A)$ an... | Why does "explaining away" make intuitive sense?
You're asking for intuition. What does it mean that $A$ and $B$ are independent? It means that if I tell you that I've just seen the monster, your opinion about the occurrence or not of the earthquake |
7,112 | Why does "explaining away" make intuitive sense? | From the linked abstract, it appears that "explaining away" is discussing a learning mechanism, a common way that humans reason, not a formal method of logic or probability. It's a human-like way of reasoning that's not formally correct, just as inductive reasoning is not formally correct (as opposed to deductive reaso... | Why does "explaining away" make intuitive sense? | From the linked abstract, it appears that "explaining away" is discussing a learning mechanism, a common way that humans reason, not a formal method of logic or probability. It's a human-like way of r | Why does "explaining away" make intuitive sense?
From the linked abstract, it appears that "explaining away" is discussing a learning mechanism, a common way that humans reason, not a formal method of logic or probability. It's a human-like way of reasoning that's not formally correct, just as inductive reasoning is no... | Why does "explaining away" make intuitive sense?
From the linked abstract, it appears that "explaining away" is discussing a learning mechanism, a common way that humans reason, not a formal method of logic or probability. It's a human-like way of r |
7,113 | Why does "explaining away" make intuitive sense? | I think an easier way to think of it is: If there is any variable $C$ $(0<P(C)<1)$ such that the occurrence of $C$ increases the probability of both $A$ and $B$, then $A$ and $B$ cannot be independent. In your example, you actually chose variables that you intuitively understand to be dependent, not independent. That i... | Why does "explaining away" make intuitive sense? | I think an easier way to think of it is: If there is any variable $C$ $(0<P(C)<1)$ such that the occurrence of $C$ increases the probability of both $A$ and $B$, then $A$ and $B$ cannot be independent | Why does "explaining away" make intuitive sense?
I think an easier way to think of it is: If there is any variable $C$ $(0<P(C)<1)$ such that the occurrence of $C$ increases the probability of both $A$ and $B$, then $A$ and $B$ cannot be independent. In your example, you actually chose variables that you intuitively un... | Why does "explaining away" make intuitive sense?
I think an easier way to think of it is: If there is any variable $C$ $(0<P(C)<1)$ such that the occurrence of $C$ increases the probability of both $A$ and $B$, then $A$ and $B$ cannot be independent |
7,114 | Are there algorithms for computing "running" linear or logistic regression parameters? | The linear régression coefficients of $y = ax + b$ are $a = cov(x,y)/var(x)$ and $b = mean(y) - a \cdot mean(x)$.
So all you really need is an incremental method to compute $cov(x,y)$. From this value and the variance of $x$ and the mean of both $y$ and $x$ you can compute the parameters $a$ and $b$.
As you will see in... | Are there algorithms for computing "running" linear or logistic regression parameters? | The linear régression coefficients of $y = ax + b$ are $a = cov(x,y)/var(x)$ and $b = mean(y) - a \cdot mean(x)$.
So all you really need is an incremental method to compute $cov(x,y)$. From this value | Are there algorithms for computing "running" linear or logistic regression parameters?
The linear régression coefficients of $y = ax + b$ are $a = cov(x,y)/var(x)$ and $b = mean(y) - a \cdot mean(x)$.
So all you really need is an incremental method to compute $cov(x,y)$. From this value and the variance of $x$ and the ... | Are there algorithms for computing "running" linear or logistic regression parameters?
The linear régression coefficients of $y = ax + b$ are $a = cov(x,y)/var(x)$ and $b = mean(y) - a \cdot mean(x)$.
So all you really need is an incremental method to compute $cov(x,y)$. From this value |
7,115 | Are there algorithms for computing "running" linear or logistic regression parameters? | For your two specific examples:
Linear Regression
The paper "Online Linear Regression and Its Application to Model-Based Reinforcement Learning" by Alexander Strehl and Michael Littman describes an algorithm called "KWIK Linear Regression" (see algorithm 1) which provides an approximation to the linear regression solut... | Are there algorithms for computing "running" linear or logistic regression parameters? | For your two specific examples:
Linear Regression
The paper "Online Linear Regression and Its Application to Model-Based Reinforcement Learning" by Alexander Strehl and Michael Littman describes an al | Are there algorithms for computing "running" linear or logistic regression parameters?
For your two specific examples:
Linear Regression
The paper "Online Linear Regression and Its Application to Model-Based Reinforcement Learning" by Alexander Strehl and Michael Littman describes an algorithm called "KWIK Linear Regre... | Are there algorithms for computing "running" linear or logistic regression parameters?
For your two specific examples:
Linear Regression
The paper "Online Linear Regression and Its Application to Model-Based Reinforcement Learning" by Alexander Strehl and Michael Littman describes an al |
7,116 | Are there algorithms for computing "running" linear or logistic regression parameters? | As a general principle:
0) you keep the sufficient statistics and the current ML estimates
1) when you get new data, update the sufficient statistics and the estimates
2) When you don't have sufficient statistics you'll need to use all of the data.
3) Typically you don't have closed-form solutions; use the previous MLE... | Are there algorithms for computing "running" linear or logistic regression parameters? | As a general principle:
0) you keep the sufficient statistics and the current ML estimates
1) when you get new data, update the sufficient statistics and the estimates
2) When you don't have sufficien | Are there algorithms for computing "running" linear or logistic regression parameters?
As a general principle:
0) you keep the sufficient statistics and the current ML estimates
1) when you get new data, update the sufficient statistics and the estimates
2) When you don't have sufficient statistics you'll need to use a... | Are there algorithms for computing "running" linear or logistic regression parameters?
As a general principle:
0) you keep the sufficient statistics and the current ML estimates
1) when you get new data, update the sufficient statistics and the estimates
2) When you don't have sufficien |
7,117 | Are there algorithms for computing "running" linear or logistic regression parameters? | Adding to tdc's answer, there are no known methods to compute exact estimates of the coefficients at any point in time with just constant time per iteration. However, there are some alternatives which are reasonable and interesting.
The first model to look at is the online learning setting. In this setting, the world f... | Are there algorithms for computing "running" linear or logistic regression parameters? | Adding to tdc's answer, there are no known methods to compute exact estimates of the coefficients at any point in time with just constant time per iteration. However, there are some alternatives which | Are there algorithms for computing "running" linear or logistic regression parameters?
Adding to tdc's answer, there are no known methods to compute exact estimates of the coefficients at any point in time with just constant time per iteration. However, there are some alternatives which are reasonable and interesting.
... | Are there algorithms for computing "running" linear or logistic regression parameters?
Adding to tdc's answer, there are no known methods to compute exact estimates of the coefficients at any point in time with just constant time per iteration. However, there are some alternatives which |
7,118 | Are there algorithms for computing "running" linear or logistic regression parameters? | You can use some standard Kalman Filter package in R for this - sspir, dlm, kfas, etc. I feel that KF is a much more developed area than online-learning, so it may be more practical. You may use a model
$$y_t = \beta_t\cdot x_t + \varepsilon_t, \\ \beta_t = \beta_{t-1}+ \eta_t$$ to allow your regression coefficients to... | Are there algorithms for computing "running" linear or logistic regression parameters? | You can use some standard Kalman Filter package in R for this - sspir, dlm, kfas, etc. I feel that KF is a much more developed area than online-learning, so it may be more practical. You may use a mod | Are there algorithms for computing "running" linear or logistic regression parameters?
You can use some standard Kalman Filter package in R for this - sspir, dlm, kfas, etc. I feel that KF is a much more developed area than online-learning, so it may be more practical. You may use a model
$$y_t = \beta_t\cdot x_t + \va... | Are there algorithms for computing "running" linear or logistic regression parameters?
You can use some standard Kalman Filter package in R for this - sspir, dlm, kfas, etc. I feel that KF is a much more developed area than online-learning, so it may be more practical. You may use a mod |
7,119 | Are there algorithms for computing "running" linear or logistic regression parameters? | Other answers have pointed to the world of machine learning, and that is certainly one place where this problem has been addressed.
However, another approach that may be better suited to your needs is the use of the QR factorization with with low rank updates. Approaches to doing this and using it to solve least squ... | Are there algorithms for computing "running" linear or logistic regression parameters? | Other answers have pointed to the world of machine learning, and that is certainly one place where this problem has been addressed.
However, another approach that may be better suited to your needs | Are there algorithms for computing "running" linear or logistic regression parameters?
Other answers have pointed to the world of machine learning, and that is certainly one place where this problem has been addressed.
However, another approach that may be better suited to your needs is the use of the QR factorizatio... | Are there algorithms for computing "running" linear or logistic regression parameters?
Other answers have pointed to the world of machine learning, and that is certainly one place where this problem has been addressed.
However, another approach that may be better suited to your needs |
7,120 | Are there algorithms for computing "running" linear or logistic regression parameters? | This is to add to @chmike answer.
The method appears to be similar to B. P. Welford’s online algorithm for standard deviation which also calculates the mean. John Cook gives a good explanation here. Tony Finch in 2009 provides a method for an exponential moving average and standard deviation:
diff := x – mean
incr :=... | Are there algorithms for computing "running" linear or logistic regression parameters? | This is to add to @chmike answer.
The method appears to be similar to B. P. Welford’s online algorithm for standard deviation which also calculates the mean. John Cook gives a good explanation here. | Are there algorithms for computing "running" linear or logistic regression parameters?
This is to add to @chmike answer.
The method appears to be similar to B. P. Welford’s online algorithm for standard deviation which also calculates the mean. John Cook gives a good explanation here. Tony Finch in 2009 provides a met... | Are there algorithms for computing "running" linear or logistic regression parameters?
This is to add to @chmike answer.
The method appears to be similar to B. P. Welford’s online algorithm for standard deviation which also calculates the mean. John Cook gives a good explanation here. |
7,121 | How to sample from a discrete distribution? [duplicate] | Sure. Here's an R function that will sample from that distribution n times, with replacement:
sampleDist = function(n) {
sample(x = c(1,2,3,4), n, replace = T, prob = c(0.1, 0.4, 0.2, 0.3))
}
# > sampleDist(10)
# [1] 4 2 2 2 2 2 4 1 2 2
If you want to go a little lower level, you can see the actual algorithm u... | How to sample from a discrete distribution? [duplicate] | Sure. Here's an R function that will sample from that distribution n times, with replacement:
sampleDist = function(n) {
sample(x = c(1,2,3,4), n, replace = T, prob = c(0.1, 0.4, 0.2, 0.3))
}
| How to sample from a discrete distribution? [duplicate]
Sure. Here's an R function that will sample from that distribution n times, with replacement:
sampleDist = function(n) {
sample(x = c(1,2,3,4), n, replace = T, prob = c(0.1, 0.4, 0.2, 0.3))
}
# > sampleDist(10)
# [1] 4 2 2 2 2 2 4 1 2 2
If you want to go ... | How to sample from a discrete distribution? [duplicate]
Sure. Here's an R function that will sample from that distribution n times, with replacement:
sampleDist = function(n) {
sample(x = c(1,2,3,4), n, replace = T, prob = c(0.1, 0.4, 0.2, 0.3))
}
|
7,122 | How to sample from a discrete distribution? [duplicate] | In response to a question in comments, here's an outline of a few potentially* faster ways to do discrete distributions than the cdf method.
* I say "potentially" because for some discrete cases a well implemented inverse-cdf approach can be very fast. The general case is harder to make fast without introducing additio... | How to sample from a discrete distribution? [duplicate] | In response to a question in comments, here's an outline of a few potentially* faster ways to do discrete distributions than the cdf method.
* I say "potentially" because for some discrete cases a wel | How to sample from a discrete distribution? [duplicate]
In response to a question in comments, here's an outline of a few potentially* faster ways to do discrete distributions than the cdf method.
* I say "potentially" because for some discrete cases a well implemented inverse-cdf approach can be very fast. The general... | How to sample from a discrete distribution? [duplicate]
In response to a question in comments, here's an outline of a few potentially* faster ways to do discrete distributions than the cdf method.
* I say "potentially" because for some discrete cases a wel |
7,123 | How to sample from a discrete distribution? [duplicate] | In python you could do something like
from scipy.stats import rv_discrete
x=[1,2,3,4]
px=[0.1,0.4,0.2,0.3]
sample=rv_discrete(values=(x,px)).rvs(size=10)
Which would give you 10 samples from the distribution. You could repeat this then find the proportions of samples that are 2. | How to sample from a discrete distribution? [duplicate] | In python you could do something like
from scipy.stats import rv_discrete
x=[1,2,3,4]
px=[0.1,0.4,0.2,0.3]
sample=rv_discrete(values=(x,px)).rvs(size=10)
Which would give you 10 sam | How to sample from a discrete distribution? [duplicate]
In python you could do something like
from scipy.stats import rv_discrete
x=[1,2,3,4]
px=[0.1,0.4,0.2,0.3]
sample=rv_discrete(values=(x,px)).rvs(size=10)
Which would give you 10 samples from the distribution. You could repeat this then find the... | How to sample from a discrete distribution? [duplicate]
In python you could do something like
from scipy.stats import rv_discrete
x=[1,2,3,4]
px=[0.1,0.4,0.2,0.3]
sample=rv_discrete(values=(x,px)).rvs(size=10)
Which would give you 10 sam |
7,124 | How to sample from a discrete distribution? [duplicate] | Yes it is possible and fairly easy, exactly how depends on what tool(s) you are using.
In R it would be sample(1:4, n, prob=c(0.1,0.4,0.2,0.3), replace=TRUE) where n is the number of values you want to sample.
In tools without an equivalent function you can still generate a uniform value and then your RV will equal 1 i... | How to sample from a discrete distribution? [duplicate] | Yes it is possible and fairly easy, exactly how depends on what tool(s) you are using.
In R it would be sample(1:4, n, prob=c(0.1,0.4,0.2,0.3), replace=TRUE) where n is the number of values you want t | How to sample from a discrete distribution? [duplicate]
Yes it is possible and fairly easy, exactly how depends on what tool(s) you are using.
In R it would be sample(1:4, n, prob=c(0.1,0.4,0.2,0.3), replace=TRUE) where n is the number of values you want to sample.
In tools without an equivalent function you can still ... | How to sample from a discrete distribution? [duplicate]
Yes it is possible and fairly easy, exactly how depends on what tool(s) you are using.
In R it would be sample(1:4, n, prob=c(0.1,0.4,0.2,0.3), replace=TRUE) where n is the number of values you want t |
7,125 | How to sample from a discrete distribution? [duplicate] | In Stata:
In Mata use rdiscrete() as documented at http://www.stata.com/help.cgi?mf_runiform
In Stata itself, there are various ways. Here's one:
. gen rnd = runiform()
. gen y = cond(rnd <= 0.1, 1, cond(rnd <= .5, 2, cond(rnd <= .7, 3, 4))) | How to sample from a discrete distribution? [duplicate] | In Stata:
In Mata use rdiscrete() as documented at http://www.stata.com/help.cgi?mf_runiform
In Stata itself, there are various ways. Here's one:
. gen rnd = runiform()
. gen y = cond(rnd <= 0.1, 1 | How to sample from a discrete distribution? [duplicate]
In Stata:
In Mata use rdiscrete() as documented at http://www.stata.com/help.cgi?mf_runiform
In Stata itself, there are various ways. Here's one:
. gen rnd = runiform()
. gen y = cond(rnd <= 0.1, 1, cond(rnd <= .5, 2, cond(rnd <= .7, 3, 4))) | How to sample from a discrete distribution? [duplicate]
In Stata:
In Mata use rdiscrete() as documented at http://www.stata.com/help.cgi?mf_runiform
In Stata itself, there are various ways. Here's one:
. gen rnd = runiform()
. gen y = cond(rnd <= 0.1, 1 |
7,126 | Gradient of Hinge loss | To get the gradient we differentiate the loss with respect to $i$th component of $w$.
Rewrite hinge loss in terms of $w$ as $f(g(w))$ where $f(z)=\max(0,1-y\ z)$ and $g(w)=\mathbf{x}\cdot \mathbf{w}$
Using chain rule we get
$$\frac{\partial}{\partial w_i} f(g(w))=\frac{\partial f}{\partial z} \frac{\partial g}{\partial... | Gradient of Hinge loss | To get the gradient we differentiate the loss with respect to $i$th component of $w$.
Rewrite hinge loss in terms of $w$ as $f(g(w))$ where $f(z)=\max(0,1-y\ z)$ and $g(w)=\mathbf{x}\cdot \mathbf{w}$
| Gradient of Hinge loss
To get the gradient we differentiate the loss with respect to $i$th component of $w$.
Rewrite hinge loss in terms of $w$ as $f(g(w))$ where $f(z)=\max(0,1-y\ z)$ and $g(w)=\mathbf{x}\cdot \mathbf{w}$
Using chain rule we get
$$\frac{\partial}{\partial w_i} f(g(w))=\frac{\partial f}{\partial z} \fr... | Gradient of Hinge loss
To get the gradient we differentiate the loss with respect to $i$th component of $w$.
Rewrite hinge loss in terms of $w$ as $f(g(w))$ where $f(z)=\max(0,1-y\ z)$ and $g(w)=\mathbf{x}\cdot \mathbf{w}$
|
7,127 | Gradient of Hinge loss | This is 3 years late, but still may be relevant for someone...
Let $S$ denote a sample of points $x_i \in R^d$ and the set of corresponding labels $y_i \in \{-1,1\}$. We search to find a hyperplane $w$ that would minimize the total hinge-loss:
\begin{equation}
w^* = \underset{w}{\text{argmin }} L^{hinge}_S(w) = \unders... | Gradient of Hinge loss | This is 3 years late, but still may be relevant for someone...
Let $S$ denote a sample of points $x_i \in R^d$ and the set of corresponding labels $y_i \in \{-1,1\}$. We search to find a hyperplane $w | Gradient of Hinge loss
This is 3 years late, but still may be relevant for someone...
Let $S$ denote a sample of points $x_i \in R^d$ and the set of corresponding labels $y_i \in \{-1,1\}$. We search to find a hyperplane $w$ that would minimize the total hinge-loss:
\begin{equation}
w^* = \underset{w}{\text{argmin }} L... | Gradient of Hinge loss
This is 3 years late, but still may be relevant for someone...
Let $S$ denote a sample of points $x_i \in R^d$ and the set of corresponding labels $y_i \in \{-1,1\}$. We search to find a hyperplane $w |
7,128 | Gradient of Hinge loss | I fixed your code. The main problem is your definition of hinge and d_hinge functions. These should be applied one sample at a time. Instead your definition aggregates all the samples before taking the maximum.
#Run standard gradient descent
gradient_descent<-function(fw, dfw, n, lr=0.01)
{
#Date to be used
x<... | Gradient of Hinge loss | I fixed your code. The main problem is your definition of hinge and d_hinge functions. These should be applied one sample at a time. Instead your definition aggregates all the samples before taking th | Gradient of Hinge loss
I fixed your code. The main problem is your definition of hinge and d_hinge functions. These should be applied one sample at a time. Instead your definition aggregates all the samples before taking the maximum.
#Run standard gradient descent
gradient_descent<-function(fw, dfw, n, lr=0.01)
{
... | Gradient of Hinge loss
I fixed your code. The main problem is your definition of hinge and d_hinge functions. These should be applied one sample at a time. Instead your definition aggregates all the samples before taking th |
7,129 | What is Bayes' theorem all about? | Bayes' theorem is a relatively simple, but fundamental result of probability theory that allows for the calculation of certain conditional probabilities. Conditional probabilities are just those probabilities that reflect the influence of one event on the probability of another.
Simply put, in its most famous form, i... | What is Bayes' theorem all about? | Bayes' theorem is a relatively simple, but fundamental result of probability theory that allows for the calculation of certain conditional probabilities. Conditional probabilities are just those prob | What is Bayes' theorem all about?
Bayes' theorem is a relatively simple, but fundamental result of probability theory that allows for the calculation of certain conditional probabilities. Conditional probabilities are just those probabilities that reflect the influence of one event on the probability of another.
Simp... | What is Bayes' theorem all about?
Bayes' theorem is a relatively simple, but fundamental result of probability theory that allows for the calculation of certain conditional probabilities. Conditional probabilities are just those prob |
7,130 | What is Bayes' theorem all about? | I'm sorry, but there seems to be some confusion here:
Bayes' theorem is not up for discussion of the neverending Bayesian-Frequentist debate. It is a theorem that is consistent with both schools of thought (given that it is consistent with Kolmogorov's probability axioms).
Of course, Bayes' theorem is the core of Baye... | What is Bayes' theorem all about? | I'm sorry, but there seems to be some confusion here:
Bayes' theorem is not up for discussion of the neverending Bayesian-Frequentist debate. It is a theorem that is consistent with both schools of t | What is Bayes' theorem all about?
I'm sorry, but there seems to be some confusion here:
Bayes' theorem is not up for discussion of the neverending Bayesian-Frequentist debate. It is a theorem that is consistent with both schools of thought (given that it is consistent with Kolmogorov's probability axioms).
Of course, ... | What is Bayes' theorem all about?
I'm sorry, but there seems to be some confusion here:
Bayes' theorem is not up for discussion of the neverending Bayesian-Frequentist debate. It is a theorem that is consistent with both schools of t |
7,131 | What is Bayes' theorem all about? | Bayes' theorem is a way to rotate a conditional probability $P(A|B)$ to another conditional probability $P(B|A)$.
A stumbling block for some is the meaning of $P(B|A)$. This is a way to reduce the space of possible events by considering only those events where $A$ definitely happens (or is true). So for instance the ... | What is Bayes' theorem all about? | Bayes' theorem is a way to rotate a conditional probability $P(A|B)$ to another conditional probability $P(B|A)$.
A stumbling block for some is the meaning of $P(B|A)$. This is a way to reduce the sp | What is Bayes' theorem all about?
Bayes' theorem is a way to rotate a conditional probability $P(A|B)$ to another conditional probability $P(B|A)$.
A stumbling block for some is the meaning of $P(B|A)$. This is a way to reduce the space of possible events by considering only those events where $A$ definitely happens (... | What is Bayes' theorem all about?
Bayes' theorem is a way to rotate a conditional probability $P(A|B)$ to another conditional probability $P(B|A)$.
A stumbling block for some is the meaning of $P(B|A)$. This is a way to reduce the sp |
7,132 | What is Bayes' theorem all about? | There are two main schools of thought is Statistics: frequentist and Bayesian.
Bayes theorem is to do with the latter and can be seen as a way of understanding how the probability that a theory is true is affected by a new piece of evidence. This is known as conditional probability. You might want to look at this to g... | What is Bayes' theorem all about? | There are two main schools of thought is Statistics: frequentist and Bayesian.
Bayes theorem is to do with the latter and can be seen as a way of understanding how the probability that a theory is tr | What is Bayes' theorem all about?
There are two main schools of thought is Statistics: frequentist and Bayesian.
Bayes theorem is to do with the latter and can be seen as a way of understanding how the probability that a theory is true is affected by a new piece of evidence. This is known as conditional probability. Y... | What is Bayes' theorem all about?
There are two main schools of thought is Statistics: frequentist and Bayesian.
Bayes theorem is to do with the latter and can be seen as a way of understanding how the probability that a theory is tr |
7,133 | What is Bayes' theorem all about? | Let me give you a very very intuitional insight. Suppose you are tossing a coin 10 times and you get 8 heads and 2 tails. The question that would come to your mind is whether this coin is biased towards heads or not.
Now if you go by conventional definitions or the frequentist approach of probability you might say th... | What is Bayes' theorem all about? | Let me give you a very very intuitional insight. Suppose you are tossing a coin 10 times and you get 8 heads and 2 tails. The question that would come to your mind is whether this coin is biased towa | What is Bayes' theorem all about?
Let me give you a very very intuitional insight. Suppose you are tossing a coin 10 times and you get 8 heads and 2 tails. The question that would come to your mind is whether this coin is biased towards heads or not.
Now if you go by conventional definitions or the frequentist approa... | What is Bayes' theorem all about?
Let me give you a very very intuitional insight. Suppose you are tossing a coin 10 times and you get 8 heads and 2 tails. The question that would come to your mind is whether this coin is biased towa |
7,134 | What is Bayes' theorem all about? | Bayes' theorem relates two ideas: probability and likelihood. Probability says: given this model, these are the outcomes. So: given a fair coin, I'll get heads 50% of the time. Likelihood says: given these outcomes, this is what we can say about the model. So: if you toss a coin 100 times and get 88 heads (to pick up o... | What is Bayes' theorem all about? | Bayes' theorem relates two ideas: probability and likelihood. Probability says: given this model, these are the outcomes. So: given a fair coin, I'll get heads 50% of the time. Likelihood says: given | What is Bayes' theorem all about?
Bayes' theorem relates two ideas: probability and likelihood. Probability says: given this model, these are the outcomes. So: given a fair coin, I'll get heads 50% of the time. Likelihood says: given these outcomes, this is what we can say about the model. So: if you toss a coin 100 ti... | What is Bayes' theorem all about?
Bayes' theorem relates two ideas: probability and likelihood. Probability says: given this model, these are the outcomes. So: given a fair coin, I'll get heads 50% of the time. Likelihood says: given |
7,135 | What is Bayes' theorem all about? | Bayes theorem in its most obvious form is simply a re-statement of two things:
the joint probability is symmetric in its arguments $P(HD|I)=P(DH|I)$
the product rule $P(HD|I)=P(H|I)P(D|HI)$
So by using the symmetry:
$$P(HD|I)=P(H|I)P(D|HI)=P(D|I)P(H|DI)$$
Now if $P(D|I) \neq 0$ you can divide both sides by $P(D|I)$ t... | What is Bayes' theorem all about? | Bayes theorem in its most obvious form is simply a re-statement of two things:
the joint probability is symmetric in its arguments $P(HD|I)=P(DH|I)$
the product rule $P(HD|I)=P(H|I)P(D|HI)$
So by us | What is Bayes' theorem all about?
Bayes theorem in its most obvious form is simply a re-statement of two things:
the joint probability is symmetric in its arguments $P(HD|I)=P(DH|I)$
the product rule $P(HD|I)=P(H|I)P(D|HI)$
So by using the symmetry:
$$P(HD|I)=P(H|I)P(D|HI)=P(D|I)P(H|DI)$$
Now if $P(D|I) \neq 0$ you c... | What is Bayes' theorem all about?
Bayes theorem in its most obvious form is simply a re-statement of two things:
the joint probability is symmetric in its arguments $P(HD|I)=P(DH|I)$
the product rule $P(HD|I)=P(H|I)P(D|HI)$
So by us |
7,136 | What is Bayes' theorem all about? | I really like Kevin Murphy's intro the to Bayes Theorem
http://www.cs.ubc.ca/~murphyk/Bayes/bayesrule.html
The quote here is from an economist article:
http://www.cs.ubc.ca/~murphyk/Bayes/economist.html
The essence of the Bayesian approach is to provide a mathematical rule explaining how you should change your existin... | What is Bayes' theorem all about? | I really like Kevin Murphy's intro the to Bayes Theorem
http://www.cs.ubc.ca/~murphyk/Bayes/bayesrule.html
The quote here is from an economist article:
http://www.cs.ubc.ca/~murphyk/Bayes/economist.ht | What is Bayes' theorem all about?
I really like Kevin Murphy's intro the to Bayes Theorem
http://www.cs.ubc.ca/~murphyk/Bayes/bayesrule.html
The quote here is from an economist article:
http://www.cs.ubc.ca/~murphyk/Bayes/economist.html
The essence of the Bayesian approach is to provide a mathematical rule explaining ... | What is Bayes' theorem all about?
I really like Kevin Murphy's intro the to Bayes Theorem
http://www.cs.ubc.ca/~murphyk/Bayes/bayesrule.html
The quote here is from an economist article:
http://www.cs.ubc.ca/~murphyk/Bayes/economist.ht |
7,137 | Introduction to causal analysis | Pearl recently published a new book, aimed for beginners: Causal Inference in Statistics: A Primer. If you have never seen causality with directed acyclic graphs before, this is where you should start. And you should do all the study questions of the book —— this will help you get acquainted with the new tools and not... | Introduction to causal analysis | Pearl recently published a new book, aimed for beginners: Causal Inference in Statistics: A Primer. If you have never seen causality with directed acyclic graphs before, this is where you should start | Introduction to causal analysis
Pearl recently published a new book, aimed for beginners: Causal Inference in Statistics: A Primer. If you have never seen causality with directed acyclic graphs before, this is where you should start. And you should do all the study questions of the book —— this will help you get acqua... | Introduction to causal analysis
Pearl recently published a new book, aimed for beginners: Causal Inference in Statistics: A Primer. If you have never seen causality with directed acyclic graphs before, this is where you should start |
7,138 | Introduction to causal analysis | Try Morgan and Winship (2007) for a social science take or Hernan and Robins (forthcoming) for an epidemiological take. Although still in progress, this looks like it's going to be very good.
Morgan and Winship is particularly good on what must be assumed for causal interpretations of regression-type models.
Pearl (... | Introduction to causal analysis | Try Morgan and Winship (2007) for a social science take or Hernan and Robins (forthcoming) for an epidemiological take. Although still in progress, this looks like it's going to be very good.
Morgan | Introduction to causal analysis
Try Morgan and Winship (2007) for a social science take or Hernan and Robins (forthcoming) for an epidemiological take. Although still in progress, this looks like it's going to be very good.
Morgan and Winship is particularly good on what must be assumed for causal interpretations of r... | Introduction to causal analysis
Try Morgan and Winship (2007) for a social science take or Hernan and Robins (forthcoming) for an epidemiological take. Although still in progress, this looks like it's going to be very good.
Morgan |
7,139 | Introduction to causal analysis | I have very high expectations for Austin Nichols' forthcoming book Causal Inference: Measuring the Effect of x on y. The expected publication date is 2013. In the mean time, his handout and paper provide a nice overview of panel methods, instrumental variables, propensity score matching/reweighting, and regression dis... | Introduction to causal analysis | I have very high expectations for Austin Nichols' forthcoming book Causal Inference: Measuring the Effect of x on y. The expected publication date is 2013. In the mean time, his handout and paper pro | Introduction to causal analysis
I have very high expectations for Austin Nichols' forthcoming book Causal Inference: Measuring the Effect of x on y. The expected publication date is 2013. In the mean time, his handout and paper provide a nice overview of panel methods, instrumental variables, propensity score matching... | Introduction to causal analysis
I have very high expectations for Austin Nichols' forthcoming book Causal Inference: Measuring the Effect of x on y. The expected publication date is 2013. In the mean time, his handout and paper pro |
7,140 | Introduction to causal analysis | Cosma Shalizi's textbook Advanced Data Analysis from an Elementary Point of View has an excellent coverage of causation. (The textbook is still in draft form, and is available online as a pdf, so it has the added benefit of being free.)
You should decide, though, whether you are interested in methods for (a) estimating... | Introduction to causal analysis | Cosma Shalizi's textbook Advanced Data Analysis from an Elementary Point of View has an excellent coverage of causation. (The textbook is still in draft form, and is available online as a pdf, so it h | Introduction to causal analysis
Cosma Shalizi's textbook Advanced Data Analysis from an Elementary Point of View has an excellent coverage of causation. (The textbook is still in draft form, and is available online as a pdf, so it has the added benefit of being free.)
You should decide, though, whether you are interest... | Introduction to causal analysis
Cosma Shalizi's textbook Advanced Data Analysis from an Elementary Point of View has an excellent coverage of causation. (The textbook is still in draft form, and is available online as a pdf, so it h |
7,141 | Introduction to causal analysis | I'd recommend:
Data Analysis Using Regression and Multilevel/Hierarchical Models (Gelman & Hill)
Chapter9 and Chapter10 are about causal inference and publicly accessible.
Gelman is known to be a great author who describes complex concepts thoroughly.
Also consider his web blog: http://andrewgelman.com/ there are lots... | Introduction to causal analysis | I'd recommend:
Data Analysis Using Regression and Multilevel/Hierarchical Models (Gelman & Hill)
Chapter9 and Chapter10 are about causal inference and publicly accessible.
Gelman is known to be a gre | Introduction to causal analysis
I'd recommend:
Data Analysis Using Regression and Multilevel/Hierarchical Models (Gelman & Hill)
Chapter9 and Chapter10 are about causal inference and publicly accessible.
Gelman is known to be a great author who describes complex concepts thoroughly.
Also consider his web blog: http://... | Introduction to causal analysis
I'd recommend:
Data Analysis Using Regression and Multilevel/Hierarchical Models (Gelman & Hill)
Chapter9 and Chapter10 are about causal inference and publicly accessible.
Gelman is known to be a gre |
7,142 | What is a Highest Density Region (HDR)? | I recommend Rob Hyndman's 1996 article "Computing and Graphing Highest Density Regions" in The American Statistician. Here is the definition of the HDR, taken from that article:
Let $f(x)$ be the density function of a random variable $X$. Then the
$100(1-\alpha)\%$ HDR is the subset $R(f_\alpha)$ of the sample space... | What is a Highest Density Region (HDR)? | I recommend Rob Hyndman's 1996 article "Computing and Graphing Highest Density Regions" in The American Statistician. Here is the definition of the HDR, taken from that article:
Let $f(x)$ be the den | What is a Highest Density Region (HDR)?
I recommend Rob Hyndman's 1996 article "Computing and Graphing Highest Density Regions" in The American Statistician. Here is the definition of the HDR, taken from that article:
Let $f(x)$ be the density function of a random variable $X$. Then the
$100(1-\alpha)\%$ HDR is the ... | What is a Highest Density Region (HDR)?
I recommend Rob Hyndman's 1996 article "Computing and Graphing Highest Density Regions" in The American Statistician. Here is the definition of the HDR, taken from that article:
Let $f(x)$ be the den |
7,143 | What is a Highest Density Region (HDR)? | A highest posterior density [interval] is basically the shortest interval on a posterior density for some given confidence level. A highest density region is probably the same idea applied to any arbitrary density, so not necessarily a posterior distribution.
If $1-\alpha$ is your confidence level, you can always find... | What is a Highest Density Region (HDR)? | A highest posterior density [interval] is basically the shortest interval on a posterior density for some given confidence level. A highest density region is probably the same idea applied to any arbi | What is a Highest Density Region (HDR)?
A highest posterior density [interval] is basically the shortest interval on a posterior density for some given confidence level. A highest density region is probably the same idea applied to any arbitrary density, so not necessarily a posterior distribution.
If $1-\alpha$ is yo... | What is a Highest Density Region (HDR)?
A highest posterior density [interval] is basically the shortest interval on a posterior density for some given confidence level. A highest density region is probably the same idea applied to any arbi |
7,144 | What is a Highest Density Region (HDR)? | Hyndman (1996):
The region covering the sample space for a given probability 1-α, should have the smallest possible volume.
Every point inside the region should have probability density at least as large as every point outside the region.
such regions are called highest density regions (HDR’s)
One of the most distinc... | What is a Highest Density Region (HDR)? | Hyndman (1996):
The region covering the sample space for a given probability 1-α, should have the smallest possible volume.
Every point inside the region should have probability density at least as l | What is a Highest Density Region (HDR)?
Hyndman (1996):
The region covering the sample space for a given probability 1-α, should have the smallest possible volume.
Every point inside the region should have probability density at least as large as every point outside the region.
such regions are called highest density... | What is a Highest Density Region (HDR)?
Hyndman (1996):
The region covering the sample space for a given probability 1-α, should have the smallest possible volume.
Every point inside the region should have probability density at least as l |
7,145 | What is a Highest Density Region (HDR)? | I don't have enough reputation to comment, but I think the current top answer by @StephanKolassa may contain a mistake?
"The idea in one dimension is to take a horizontal line and shift it up (to 𝑦=𝑓𝛼) until the area above it and under the density is 1−𝛼. Then the HDR 𝑅𝛼 is the projection to the 𝑥 axis of this a... | What is a Highest Density Region (HDR)? | I don't have enough reputation to comment, but I think the current top answer by @StephanKolassa may contain a mistake?
"The idea in one dimension is to take a horizontal line and shift it up (to 𝑦=𝑓𝛼 | What is a Highest Density Region (HDR)?
I don't have enough reputation to comment, but I think the current top answer by @StephanKolassa may contain a mistake?
"The idea in one dimension is to take a horizontal line and shift it up (to 𝑦=𝑓𝛼) until the area above it and under the density is 1−𝛼. Then the HDR 𝑅𝛼 is... | What is a Highest Density Region (HDR)?
I don't have enough reputation to comment, but I think the current top answer by @StephanKolassa may contain a mistake?
"The idea in one dimension is to take a horizontal line and shift it up (to 𝑦=𝑓𝛼 |
7,146 | What is a Highest Density Region (HDR)? | This should be the answer for this question:
Highest density regions are often the most appropriate subset to use to summarize a
region, and are capable of exposing the most striking features of the data than most alternative methods (Hyndman, 1996)
Hyndman further argues that highest-density regions (HDR) are a “more ... | What is a Highest Density Region (HDR)? | This should be the answer for this question:
Highest density regions are often the most appropriate subset to use to summarize a
region, and are capable of exposing the most striking features of the d | What is a Highest Density Region (HDR)?
This should be the answer for this question:
Highest density regions are often the most appropriate subset to use to summarize a
region, and are capable of exposing the most striking features of the data than most alternative methods (Hyndman, 1996)
Hyndman further argues that hi... | What is a Highest Density Region (HDR)?
This should be the answer for this question:
Highest density regions are often the most appropriate subset to use to summarize a
region, and are capable of exposing the most striking features of the d |
7,147 | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each person and add them? | Assuming that the decisions of invited persons to come to the wedding are independent, the number of guests that will come to the wedding can be modeled as the sum of Bernoulli random variables that have not necessarily identical probabilities of success.
This corresponds to the Poisson binomial distribution.
Let $X$ b... | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each pers | Assuming that the decisions of invited persons to come to the wedding are independent, the number of guests that will come to the wedding can be modeled as the sum of Bernoulli random variables that h | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each person and add them?
Assuming that the decisions of invited persons to come to the wedding are independent, the number of guests that will come to the wedding can be modeled as the sum of Bernoulli random variables that have... | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each pers
Assuming that the decisions of invited persons to come to the wedding are independent, the number of guests that will come to the wedding can be modeled as the sum of Bernoulli random variables that h |
7,148 | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each person and add them? | As has been pointed out, the expectations simply add.
However, knowing the expectation isn't much use, you also need some sense of the likely variation around it.
There are three things you need to be concerned about:
variation in the individuals around their expectation (a person with 60% chance of coming doesn't act... | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each pers | As has been pointed out, the expectations simply add.
However, knowing the expectation isn't much use, you also need some sense of the likely variation around it.
There are three things you need to be | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each person and add them?
As has been pointed out, the expectations simply add.
However, knowing the expectation isn't much use, you also need some sense of the likely variation around it.
There are three things you need to be co... | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each pers
As has been pointed out, the expectations simply add.
However, knowing the expectation isn't much use, you also need some sense of the likely variation around it.
There are three things you need to be |
7,149 | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each person and add them? | (Ignore my earlier comment on this - I just realised I was confusing the expectation with something else.) Given that you're essentially trying to find the expectation of the number of people showing up, you can theoretically add the probability of each person showing up to do so.
This is because we can consider someon... | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each pers | (Ignore my earlier comment on this - I just realised I was confusing the expectation with something else.) Given that you're essentially trying to find the expectation of the number of people showing | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each person and add them?
(Ignore my earlier comment on this - I just realised I was confusing the expectation with something else.) Given that you're essentially trying to find the expectation of the number of people showing up,... | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each pers
(Ignore my earlier comment on this - I just realised I was confusing the expectation with something else.) Given that you're essentially trying to find the expectation of the number of people showing |
7,150 | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each person and add them? | For large numbers, 80% is what you'd expect. This may be a situation where a detailed analysis as you propose only adds errors to the calculations.
For example, is Marc's potential attendance really 1/3 of Joseph's? And is Joseph's really 30%, or might it be 25%? Things happen when you reach large numbers that simply m... | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each pers | For large numbers, 80% is what you'd expect. This may be a situation where a detailed analysis as you propose only adds errors to the calculations.
For example, is Marc's potential attendance really 1 | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each person and add them?
For large numbers, 80% is what you'd expect. This may be a situation where a detailed analysis as you propose only adds errors to the calculations.
For example, is Marc's potential attendance really 1/3 ... | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each pers
For large numbers, 80% is what you'd expect. This may be a situation where a detailed analysis as you propose only adds errors to the calculations.
For example, is Marc's potential attendance really 1 |
7,151 | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each person and add them? | As a statistician who just got married, I'll tell you that JoeTaxpayer has the right answer. The 80% figure strikes me as a little high, though could be accurate if most of the people are local (ours was a destination wedding and we landed closer to 65%).
But nonetheless, you're assuming a lot of variability in the pr... | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each pers | As a statistician who just got married, I'll tell you that JoeTaxpayer has the right answer. The 80% figure strikes me as a little high, though could be accurate if most of the people are local (ours | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each person and add them?
As a statistician who just got married, I'll tell you that JoeTaxpayer has the right answer. The 80% figure strikes me as a little high, though could be accurate if most of the people are local (ours was... | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each pers
As a statistician who just got married, I'll tell you that JoeTaxpayer has the right answer. The 80% figure strikes me as a little high, though could be accurate if most of the people are local (ours |
7,152 | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each person and add them? | Add up all probabilities, that's your expected number of people to come.
You have i=1..N events, each has probability $P_i$. The expected number of people to come is $\sum_i1_iP_i$, where $1_i$ - indicator variable equal to one if a person shows up, and zero otherwise.
Of course, we're assuming that whether someone com... | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each pers | Add up all probabilities, that's your expected number of people to come.
You have i=1..N events, each has probability $P_i$. The expected number of people to come is $\sum_i1_iP_i$, where $1_i$ - indi | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each person and add them?
Add up all probabilities, that's your expected number of people to come.
You have i=1..N events, each has probability $P_i$. The expected number of people to come is $\sum_i1_iP_i$, where $1_i$ - indicat... | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each pers
Add up all probabilities, that's your expected number of people to come.
You have i=1..N events, each has probability $P_i$. The expected number of people to come is $\sum_i1_iP_i$, where $1_i$ - indi |
7,153 | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each person and add them? | For my wedding, I made two lists -- likely to attend (80%) and unlikely to attend (20%). Regardless of any more refined assessment for any reason, I assigned everyone invited to one of the two groups. I was off by 2 people. N = 1. Purely heuristic. | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each pers | For my wedding, I made two lists -- likely to attend (80%) and unlikely to attend (20%). Regardless of any more refined assessment for any reason, I assigned everyone invited to one of the two groups. | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each person and add them?
For my wedding, I made two lists -- likely to attend (80%) and unlikely to attend (20%). Regardless of any more refined assessment for any reason, I assigned everyone invited to one of the two groups. I ... | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each pers
For my wedding, I made two lists -- likely to attend (80%) and unlikely to attend (20%). Regardless of any more refined assessment for any reason, I assigned everyone invited to one of the two groups. |
7,154 | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each person and add them? | I notice that no one has pointed out that you do not need to divide by 100. Your percentages can be viewed as expected portions of a person to show up, with the understanding that, like Schrödinger's cat, you will not get parts of a person in attendance or not in attendance, but the attendance state of each person wil... | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each pers | I notice that no one has pointed out that you do not need to divide by 100. Your percentages can be viewed as expected portions of a person to show up, with the understanding that, like Schrödinger's | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each person and add them?
I notice that no one has pointed out that you do not need to divide by 100. Your percentages can be viewed as expected portions of a person to show up, with the understanding that, like Schrödinger's ca... | Help me calculate how many people will come to my wedding! Can I attribute a percentage to each pers
I notice that no one has pointed out that you do not need to divide by 100. Your percentages can be viewed as expected portions of a person to show up, with the understanding that, like Schrödinger's |
7,155 | Test accuracy higher than training. How to interpret? | I think a first step is to check whether the reported training and test performance are in fact correct.
Is the splitting during the 5-fold cross validation done in a way that yields statistically independent cv train/test sets? E.g. if there are repeated measurements in the data, do they always end up in the same se... | Test accuracy higher than training. How to interpret? | I think a first step is to check whether the reported training and test performance are in fact correct.
Is the splitting during the 5-fold cross validation done in a way that yields statistically in | Test accuracy higher than training. How to interpret?
I think a first step is to check whether the reported training and test performance are in fact correct.
Is the splitting during the 5-fold cross validation done in a way that yields statistically independent cv train/test sets? E.g. if there are repeated measurem... | Test accuracy higher than training. How to interpret?
I think a first step is to check whether the reported training and test performance are in fact correct.
Is the splitting during the 5-fold cross validation done in a way that yields statistically in |
7,156 | Test accuracy higher than training. How to interpret? | How to interpret a test accuracy higher than training set accuracy.
Most likely culprit is your train/test split percentage. Imagine if you're using 99% of the data to train, and 1% for test, then obviously testing set accuracy will be better than the testing set, 99 times out of 100. The solution here is to use 50% ... | Test accuracy higher than training. How to interpret? | How to interpret a test accuracy higher than training set accuracy.
Most likely culprit is your train/test split percentage. Imagine if you're using 99% of the data to train, and 1% for test, then ob | Test accuracy higher than training. How to interpret?
How to interpret a test accuracy higher than training set accuracy.
Most likely culprit is your train/test split percentage. Imagine if you're using 99% of the data to train, and 1% for test, then obviously testing set accuracy will be better than the testing set, ... | Test accuracy higher than training. How to interpret?
How to interpret a test accuracy higher than training set accuracy.
Most likely culprit is your train/test split percentage. Imagine if you're using 99% of the data to train, and 1% for test, then ob |
7,157 | Test accuracy higher than training. How to interpret? | There are a few serious problems with the way you have gone about this. First of all, data splitting is unreliable unless the total sample size is huge. You would get different results if you split again. Among other things you are not considering confidence intervals on accuracy estimates. Second, 5-fold cross-val... | Test accuracy higher than training. How to interpret? | There are a few serious problems with the way you have gone about this. First of all, data splitting is unreliable unless the total sample size is huge. You would get different results if you split | Test accuracy higher than training. How to interpret?
There are a few serious problems with the way you have gone about this. First of all, data splitting is unreliable unless the total sample size is huge. You would get different results if you split again. Among other things you are not considering confidence inte... | Test accuracy higher than training. How to interpret?
There are a few serious problems with the way you have gone about this. First of all, data splitting is unreliable unless the total sample size is huge. You would get different results if you split |
7,158 | Test accuracy higher than training. How to interpret? | Assuming that there is no glitch in the implementation of the algorithms, let us look at the problem.
Imagine taking a small subset from your training data and running your learning algorithm on it. It'll obviously do very well. It's always possible to extract a subset that achieves close to 98% accuracy.
Now is your t... | Test accuracy higher than training. How to interpret? | Assuming that there is no glitch in the implementation of the algorithms, let us look at the problem.
Imagine taking a small subset from your training data and running your learning algorithm on it. I | Test accuracy higher than training. How to interpret?
Assuming that there is no glitch in the implementation of the algorithms, let us look at the problem.
Imagine taking a small subset from your training data and running your learning algorithm on it. It'll obviously do very well. It's always possible to extract a sub... | Test accuracy higher than training. How to interpret?
Assuming that there is no glitch in the implementation of the algorithms, let us look at the problem.
Imagine taking a small subset from your training data and running your learning algorithm on it. I |
7,159 | Test accuracy higher than training. How to interpret? | You have too many features (1000) for the number of samples that you have (150). You need to increase your samples or decrease your number of features.
They say usually number of features ^2 = number of samples needed. So you need at least million samples. | Test accuracy higher than training. How to interpret? | You have too many features (1000) for the number of samples that you have (150). You need to increase your samples or decrease your number of features.
They say usually number of features ^2 = number | Test accuracy higher than training. How to interpret?
You have too many features (1000) for the number of samples that you have (150). You need to increase your samples or decrease your number of features.
They say usually number of features ^2 = number of samples needed. So you need at least million samples. | Test accuracy higher than training. How to interpret?
You have too many features (1000) for the number of samples that you have (150). You need to increase your samples or decrease your number of features.
They say usually number of features ^2 = number |
7,160 | Test accuracy higher than training. How to interpret? | I had the same problem with Caret package R, however, note that what we obtain by the end of training is usually the range of performance of the models trained on the cross-validated dataset. So you need to predict the training data set again with the best model (Model$bestTune) and obtain metrics (e.g. RMSE, AUC and e... | Test accuracy higher than training. How to interpret? | I had the same problem with Caret package R, however, note that what we obtain by the end of training is usually the range of performance of the models trained on the cross-validated dataset. So you n | Test accuracy higher than training. How to interpret?
I had the same problem with Caret package R, however, note that what we obtain by the end of training is usually the range of performance of the models trained on the cross-validated dataset. So you need to predict the training data set again with the best model (Mo... | Test accuracy higher than training. How to interpret?
I had the same problem with Caret package R, however, note that what we obtain by the end of training is usually the range of performance of the models trained on the cross-validated dataset. So you n |
7,161 | Test accuracy higher than training. How to interpret? | That can be happen using any ML algorithm and even custom classifiers. Try different k-fold cross validation schemes i.e. 2 or 10 fold as well. With higher k, it is expected that test error reduced. | Test accuracy higher than training. How to interpret? | That can be happen using any ML algorithm and even custom classifiers. Try different k-fold cross validation schemes i.e. 2 or 10 fold as well. With higher k, it is expected that test error reduced. | Test accuracy higher than training. How to interpret?
That can be happen using any ML algorithm and even custom classifiers. Try different k-fold cross validation schemes i.e. 2 or 10 fold as well. With higher k, it is expected that test error reduced. | Test accuracy higher than training. How to interpret?
That can be happen using any ML algorithm and even custom classifiers. Try different k-fold cross validation schemes i.e. 2 or 10 fold as well. With higher k, it is expected that test error reduced. |
7,162 | Timing functions in R [closed] | For effective timing of programs, especially when you are interested in comparing alternative solutions, you need a control! A good way is to put the procedure you're timing into a function. Call the function within a timing loop. Write a stub procedure, essentially by stripping out all the code from your function a... | Timing functions in R [closed] | For effective timing of programs, especially when you are interested in comparing alternative solutions, you need a control! A good way is to put the procedure you're timing into a function. Call th | Timing functions in R [closed]
For effective timing of programs, especially when you are interested in comparing alternative solutions, you need a control! A good way is to put the procedure you're timing into a function. Call the function within a timing loop. Write a stub procedure, essentially by stripping out al... | Timing functions in R [closed]
For effective timing of programs, especially when you are interested in comparing alternative solutions, you need a control! A good way is to put the procedure you're timing into a function. Call th |
7,163 | Timing functions in R [closed] | Regarding your two points:
It's stylistic. I like replicate() as it is functional.
I tend to focus on elapsed, i.e. the third number.
What I often do is
N <- someNumber
mean(replicate( N, system.time( f(...) )[3], trimmed=0.05) )
to get a trimmed mean of 90% of N repetitions of calling f().
(Edited, with thanks to ... | Timing functions in R [closed] | Regarding your two points:
It's stylistic. I like replicate() as it is functional.
I tend to focus on elapsed, i.e. the third number.
What I often do is
N <- someNumber
mean(replicate( N, system.ti | Timing functions in R [closed]
Regarding your two points:
It's stylistic. I like replicate() as it is functional.
I tend to focus on elapsed, i.e. the third number.
What I often do is
N <- someNumber
mean(replicate( N, system.time( f(...) )[3], trimmed=0.05) )
to get a trimmed mean of 90% of N repetitions of callin... | Timing functions in R [closed]
Regarding your two points:
It's stylistic. I like replicate() as it is functional.
I tend to focus on elapsed, i.e. the third number.
What I often do is
N <- someNumber
mean(replicate( N, system.ti |
7,164 | Timing functions in R [closed] | You can also time with timesteps returned by Sys.time; this of course measures walltime, so real time computation time. Example code:
Sys.time()->start;
replicate(N,doMeasuredComputation());
print(Sys.time()-start); | Timing functions in R [closed] | You can also time with timesteps returned by Sys.time; this of course measures walltime, so real time computation time. Example code:
Sys.time()->start;
replicate(N,doMeasuredComputation());
print(Sys | Timing functions in R [closed]
You can also time with timesteps returned by Sys.time; this of course measures walltime, so real time computation time. Example code:
Sys.time()->start;
replicate(N,doMeasuredComputation());
print(Sys.time()-start); | Timing functions in R [closed]
You can also time with timesteps returned by Sys.time; this of course measures walltime, so real time computation time. Example code:
Sys.time()->start;
replicate(N,doMeasuredComputation());
print(Sys |
7,165 | Timing functions in R [closed] | Regarding which timing metric to use, I can not add to the other responders.
Regarding the function to use, I like using the ?benchmark from the rbenchmark package. | Timing functions in R [closed] | Regarding which timing metric to use, I can not add to the other responders.
Regarding the function to use, I like using the ?benchmark from the rbenchmark package. | Timing functions in R [closed]
Regarding which timing metric to use, I can not add to the other responders.
Regarding the function to use, I like using the ?benchmark from the rbenchmark package. | Timing functions in R [closed]
Regarding which timing metric to use, I can not add to the other responders.
Regarding the function to use, I like using the ?benchmark from the rbenchmark package. |
7,166 | Timing functions in R [closed] | They do different things. Time what you wish done. replicate() returns a vector of results of each execution of the function. The for loop does not. Therefore, they're not equivalent statements.
In addition, time a number of ways you want something done. Then you can find the most efficient method. | Timing functions in R [closed] | They do different things. Time what you wish done. replicate() returns a vector of results of each execution of the function. The for loop does not. Therefore, they're not equivalent statements.
I | Timing functions in R [closed]
They do different things. Time what you wish done. replicate() returns a vector of results of each execution of the function. The for loop does not. Therefore, they're not equivalent statements.
In addition, time a number of ways you want something done. Then you can find the most ef... | Timing functions in R [closed]
They do different things. Time what you wish done. replicate() returns a vector of results of each execution of the function. The for loop does not. Therefore, they're not equivalent statements.
I |
7,167 | Measuring the "distance" between two multivariate distributions | There is also the Kullback-Leibler divergence, which is related to the Hellinger Distance you mention above. | Measuring the "distance" between two multivariate distributions | There is also the Kullback-Leibler divergence, which is related to the Hellinger Distance you mention above. | Measuring the "distance" between two multivariate distributions
There is also the Kullback-Leibler divergence, which is related to the Hellinger Distance you mention above. | Measuring the "distance" between two multivariate distributions
There is also the Kullback-Leibler divergence, which is related to the Hellinger Distance you mention above. |
7,168 | Measuring the "distance" between two multivariate distributions | Hmm, the Bhattacharyya distance seems to be what I'm looking for, though the Hellinger distance works too. | Measuring the "distance" between two multivariate distributions | Hmm, the Bhattacharyya distance seems to be what I'm looking for, though the Hellinger distance works too. | Measuring the "distance" between two multivariate distributions
Hmm, the Bhattacharyya distance seems to be what I'm looking for, though the Hellinger distance works too. | Measuring the "distance" between two multivariate distributions
Hmm, the Bhattacharyya distance seems to be what I'm looking for, though the Hellinger distance works too. |
7,169 | Measuring the "distance" between two multivariate distributions | Heuristic
Minkowski-form
Weighted-Mean-Variance (WMV)
Nonparametric test statistics
2 (Chi Square)
Kolmogorov-Smirnov (KS)
Cramer/von Mises (CvM)
Information-theory divergences
Kullback-Liebler (KL)
Jensen–Shannon divergence (metric)
Jeffrey-divergence (numerically stable and symmetric)
Ground distance measures
... | Measuring the "distance" between two multivariate distributions | Heuristic
Minkowski-form
Weighted-Mean-Variance (WMV)
Nonparametric test statistics
2 (Chi Square)
Kolmogorov-Smirnov (KS)
Cramer/von Mises (CvM)
Information-theory divergences
Kullback-Liebler ( | Measuring the "distance" between two multivariate distributions
Heuristic
Minkowski-form
Weighted-Mean-Variance (WMV)
Nonparametric test statistics
2 (Chi Square)
Kolmogorov-Smirnov (KS)
Cramer/von Mises (CvM)
Information-theory divergences
Kullback-Liebler (KL)
Jensen–Shannon divergence (metric)
Jeffrey-divergenc... | Measuring the "distance" between two multivariate distributions
Heuristic
Minkowski-form
Weighted-Mean-Variance (WMV)
Nonparametric test statistics
2 (Chi Square)
Kolmogorov-Smirnov (KS)
Cramer/von Mises (CvM)
Information-theory divergences
Kullback-Liebler ( |
7,170 | Measuring the "distance" between two multivariate distributions | The most complete survey is provided in Statistical Inference Based on Divergence Measures by Leandro Pardo, Complutense University, Chapman Hall 2006. | Measuring the "distance" between two multivariate distributions | The most complete survey is provided in Statistical Inference Based on Divergence Measures by Leandro Pardo, Complutense University, Chapman Hall 2006. | Measuring the "distance" between two multivariate distributions
The most complete survey is provided in Statistical Inference Based on Divergence Measures by Leandro Pardo, Complutense University, Chapman Hall 2006. | Measuring the "distance" between two multivariate distributions
The most complete survey is provided in Statistical Inference Based on Divergence Measures by Leandro Pardo, Complutense University, Chapman Hall 2006. |
7,171 | Measuring the "distance" between two multivariate distributions | Few more measures of "Statistical Difference"
Permutation test (by Fisher)
Central Limit Theorem & Slutsky’s theorem
Mann-Whitney-Wilcoxin test
Anderson–Darling test
Shapiro–Wilk test
Hosmer–Lemeshow test
Kuiper's test
kernelized Stein discrepancy
Jaccard similarity
Also, hierarchical clustering deals with similarity... | Measuring the "distance" between two multivariate distributions | Few more measures of "Statistical Difference"
Permutation test (by Fisher)
Central Limit Theorem & Slutsky’s theorem
Mann-Whitney-Wilcoxin test
Anderson–Darling test
Shapiro–Wilk test
Hosmer–Lemesho | Measuring the "distance" between two multivariate distributions
Few more measures of "Statistical Difference"
Permutation test (by Fisher)
Central Limit Theorem & Slutsky’s theorem
Mann-Whitney-Wilcoxin test
Anderson–Darling test
Shapiro–Wilk test
Hosmer–Lemeshow test
Kuiper's test
kernelized Stein discrepancy
Jaccar... | Measuring the "distance" between two multivariate distributions
Few more measures of "Statistical Difference"
Permutation test (by Fisher)
Central Limit Theorem & Slutsky’s theorem
Mann-Whitney-Wilcoxin test
Anderson–Darling test
Shapiro–Wilk test
Hosmer–Lemesho |
7,172 | What is the difference between an estimator and a statistic? | Definition
From Wikipedia:
A statistic [...] is a single measure of some attribute of a
sample (e.g., its arithmetic mean value).
And
[A]n estimator is a rule for calculating an estimate of a given
quantity [of the underlying distribution] based on observed data.
The important difference is:
A statistic is a funct... | What is the difference between an estimator and a statistic? | Definition
From Wikipedia:
A statistic [...] is a single measure of some attribute of a
sample (e.g., its arithmetic mean value).
And
[A]n estimator is a rule for calculating an estimate of a given | What is the difference between an estimator and a statistic?
Definition
From Wikipedia:
A statistic [...] is a single measure of some attribute of a
sample (e.g., its arithmetic mean value).
And
[A]n estimator is a rule for calculating an estimate of a given
quantity [of the underlying distribution] based on observe... | What is the difference between an estimator and a statistic?
Definition
From Wikipedia:
A statistic [...] is a single measure of some attribute of a
sample (e.g., its arithmetic mean value).
And
[A]n estimator is a rule for calculating an estimate of a given |
7,173 | What is the difference between an estimator and a statistic? | This thread is a little old, but it appears that Wikipedia may have changed its definition and if it's accurate, it explains it more clearly for me:
An "estimator" or "point estimate" is a statistic (that is, a function
of the data) that is used to infer the value of an unknown parameter
in a statistical model.
S... | What is the difference between an estimator and a statistic? | This thread is a little old, but it appears that Wikipedia may have changed its definition and if it's accurate, it explains it more clearly for me:
An "estimator" or "point estimate" is a statistic | What is the difference between an estimator and a statistic?
This thread is a little old, but it appears that Wikipedia may have changed its definition and if it's accurate, it explains it more clearly for me:
An "estimator" or "point estimate" is a statistic (that is, a function
of the data) that is used to infer t... | What is the difference between an estimator and a statistic?
This thread is a little old, but it appears that Wikipedia may have changed its definition and if it's accurate, it explains it more clearly for me:
An "estimator" or "point estimate" is a statistic |
7,174 | What is the difference between an estimator and a statistic? | "6" is an example of an estimator. Say your question was, "what is the slope of the best linear function mapping x to y?" Your answer could be "6". Or it could be $(X'X)^{-1}X'Y$. Both are estimators. Which one is better is left to you to decide.
A really good TA once explained the concept of an estimator to me ... | What is the difference between an estimator and a statistic? | "6" is an example of an estimator. Say your question was, "what is the slope of the best linear function mapping x to y?" Your answer could be "6". Or it could be $(X'X)^{-1}X'Y$. Both are estimat | What is the difference between an estimator and a statistic?
"6" is an example of an estimator. Say your question was, "what is the slope of the best linear function mapping x to y?" Your answer could be "6". Or it could be $(X'X)^{-1}X'Y$. Both are estimators. Which one is better is left to you to decide.
A rea... | What is the difference between an estimator and a statistic?
"6" is an example of an estimator. Say your question was, "what is the slope of the best linear function mapping x to y?" Your answer could be "6". Or it could be $(X'X)^{-1}X'Y$. Both are estimat |
7,175 | What is the difference between an estimator and a statistic? | Since other answers saying that they are the same give no authoritative reference, let me give you two quotes from Statistical inference handbook by Casella and Berger:
Definition 5.2.1 Let $X_1,\dots,X_n$ be a random sample of size $n$ from a population and let $T(x_1,\dots,x_n)$ be a real-valued or
vector-valued f... | What is the difference between an estimator and a statistic? | Since other answers saying that they are the same give no authoritative reference, let me give you two quotes from Statistical inference handbook by Casella and Berger:
Definition 5.2.1 Let $X_1,\dot | What is the difference between an estimator and a statistic?
Since other answers saying that they are the same give no authoritative reference, let me give you two quotes from Statistical inference handbook by Casella and Berger:
Definition 5.2.1 Let $X_1,\dots,X_n$ be a random sample of size $n$ from a population and... | What is the difference between an estimator and a statistic?
Since other answers saying that they are the same give no authoritative reference, let me give you two quotes from Statistical inference handbook by Casella and Berger:
Definition 5.2.1 Let $X_1,\dot |
7,176 | What is the difference between an estimator and a statistic? | Interesting question. Estimators and statistics do not need to be different things, though. They are different concepts.
A statistic is a function (in broad terms) in which the input is (statistical) data. The effect is that you gain a result, usually a number, from this statistic. In a more abstract term, a statistic ... | What is the difference between an estimator and a statistic? | Interesting question. Estimators and statistics do not need to be different things, though. They are different concepts.
A statistic is a function (in broad terms) in which the input is (statistical) | What is the difference between an estimator and a statistic?
Interesting question. Estimators and statistics do not need to be different things, though. They are different concepts.
A statistic is a function (in broad terms) in which the input is (statistical) data. The effect is that you gain a result, usually a numbe... | What is the difference between an estimator and a statistic?
Interesting question. Estimators and statistics do not need to be different things, though. They are different concepts.
A statistic is a function (in broad terms) in which the input is (statistical) |
7,177 | What is the difference between an estimator and a statistic? | I think a better understanding about what is a sample helps.
[Updated: Sample is a very broad concept, I was talking about "the random sample" . I don't know whether an estimator makes sense or not when the sample is not random.]
from wikipedia:
A random sample is defined as a sample where each individual member
of ... | What is the difference between an estimator and a statistic? | I think a better understanding about what is a sample helps.
[Updated: Sample is a very broad concept, I was talking about "the random sample" . I don't know whether an estimator makes sense or not wh | What is the difference between an estimator and a statistic?
I think a better understanding about what is a sample helps.
[Updated: Sample is a very broad concept, I was talking about "the random sample" . I don't know whether an estimator makes sense or not when the sample is not random.]
from wikipedia:
A random sam... | What is the difference between an estimator and a statistic?
I think a better understanding about what is a sample helps.
[Updated: Sample is a very broad concept, I was talking about "the random sample" . I don't know whether an estimator makes sense or not wh |
7,178 | What is the difference between an estimator and a statistic? | The Goal of This Piece of Writing:
What I want to do here is to provide you with the similarities and differences between the two intimately related concepts called "statistic" and "estimator". However, I do not want to go through the differences between a parameter and a statistic, which I assume is clear enough to ev... | What is the difference between an estimator and a statistic? | The Goal of This Piece of Writing:
What I want to do here is to provide you with the similarities and differences between the two intimately related concepts called "statistic" and "estimator". Howeve | What is the difference between an estimator and a statistic?
The Goal of This Piece of Writing:
What I want to do here is to provide you with the similarities and differences between the two intimately related concepts called "statistic" and "estimator". However, I do not want to go through the differences between a pa... | What is the difference between an estimator and a statistic?
The Goal of This Piece of Writing:
What I want to do here is to provide you with the similarities and differences between the two intimately related concepts called "statistic" and "estimator". Howeve |
7,179 | What is the difference between an estimator and a statistic? | New answer to an old Q:
Definition 1. A statistic is a function that maps each sample to a real number.
Every estimator is a statistic.
But we tend to call only those statistics that are used to generate estimates ("guesses") some parameter an estimator.
So for example, the t-statistic and the sample mean are BOTH stat... | What is the difference between an estimator and a statistic? | New answer to an old Q:
Definition 1. A statistic is a function that maps each sample to a real number.
Every estimator is a statistic.
But we tend to call only those statistics that are used to gener | What is the difference between an estimator and a statistic?
New answer to an old Q:
Definition 1. A statistic is a function that maps each sample to a real number.
Every estimator is a statistic.
But we tend to call only those statistics that are used to generate estimates ("guesses") some parameter an estimator.
So f... | What is the difference between an estimator and a statistic?
New answer to an old Q:
Definition 1. A statistic is a function that maps each sample to a real number.
Every estimator is a statistic.
But we tend to call only those statistics that are used to gener |
7,180 | What is the difference between an estimator and a statistic? | In hypothesis testing :
A test-statistic is about hypothesis testing. A test-statistic is a random variable given/under the null hypothesis. Now, some may call a statistic the value/measure of the test-statistic given the sample.
With these two you can get the p-value which is a measure that helps to reject or not rej... | What is the difference between an estimator and a statistic? | In hypothesis testing :
A test-statistic is about hypothesis testing. A test-statistic is a random variable given/under the null hypothesis. Now, some may call a statistic the value/measure of the te | What is the difference between an estimator and a statistic?
In hypothesis testing :
A test-statistic is about hypothesis testing. A test-statistic is a random variable given/under the null hypothesis. Now, some may call a statistic the value/measure of the test-statistic given the sample.
With these two you can get t... | What is the difference between an estimator and a statistic?
In hypothesis testing :
A test-statistic is about hypothesis testing. A test-statistic is a random variable given/under the null hypothesis. Now, some may call a statistic the value/measure of the te |
7,181 | Kernel logistic regression vs SVM | KLRs and SVMs
Classification performance is almost identical in both cases.
KLR can provide class probabilities whereas SVM is a
deterministic classifier.
KLR has a natural extension to multi-class classification whereas in SVM, there are multiple ways to extend it to multi-class classification (and it is still an a... | Kernel logistic regression vs SVM | KLRs and SVMs
Classification performance is almost identical in both cases.
KLR can provide class probabilities whereas SVM is a
deterministic classifier.
KLR has a natural extension to multi-class | Kernel logistic regression vs SVM
KLRs and SVMs
Classification performance is almost identical in both cases.
KLR can provide class probabilities whereas SVM is a
deterministic classifier.
KLR has a natural extension to multi-class classification whereas in SVM, there are multiple ways to extend it to multi-class cl... | Kernel logistic regression vs SVM
KLRs and SVMs
Classification performance is almost identical in both cases.
KLR can provide class probabilities whereas SVM is a
deterministic classifier.
KLR has a natural extension to multi-class |
7,182 | Kernel logistic regression vs SVM | Here's my take on the issue:
SVMs are a very elegant way to do classification. There's some nice theory, some beautiful math, they generalize well, and they're not too slow either. Try to use them for regression though, and it gets messy.
Here's a resource on SVM regression. Notice the extra parameters to twiddle and ... | Kernel logistic regression vs SVM | Here's my take on the issue:
SVMs are a very elegant way to do classification. There's some nice theory, some beautiful math, they generalize well, and they're not too slow either. Try to use them for | Kernel logistic regression vs SVM
Here's my take on the issue:
SVMs are a very elegant way to do classification. There's some nice theory, some beautiful math, they generalize well, and they're not too slow either. Try to use them for regression though, and it gets messy.
Here's a resource on SVM regression. Notice th... | Kernel logistic regression vs SVM
Here's my take on the issue:
SVMs are a very elegant way to do classification. There's some nice theory, some beautiful math, they generalize well, and they're not too slow either. Try to use them for |
7,183 | Kernel logistic regression vs SVM | please visit http://www.stanford.edu/~hastie/Papers/svmtalk.pdf
Some conclusions:
The classification performance is very similar.
Has limiting optimal margin properties.
Provides estimates of the class probabilities. Often these are
more useful than the classifications.
Generalizes naturally to M-class classification thro... | Kernel logistic regression vs SVM | please visit http://www.stanford.edu/~hastie/Papers/svmtalk.pdf
Some conclusions:
The classification performance is very similar.
Has limiting optimal margin properties.
Provides estimates of the class | Kernel logistic regression vs SVM
please visit http://www.stanford.edu/~hastie/Papers/svmtalk.pdf
Some conclusions:
The classification performance is very similar.
Has limiting optimal margin properties.
Provides estimates of the class probabilities. Often these are
more useful than the classifications.
Generalizes natur... | Kernel logistic regression vs SVM
please visit http://www.stanford.edu/~hastie/Papers/svmtalk.pdf
Some conclusions:
The classification performance is very similar.
Has limiting optimal margin properties.
Provides estimates of the class |
7,184 | Intuition for Conditional Expectation of $\sigma$-algebra | One way to think about conditional expectation is as a projection onto the $\sigma$-algebra $\mathscr{G}$.
(from Wikimedia commons)
This is actually rigorously true when talking about square-integrable random variables; in this case $\mathbb{E}[\xi|\mathscr{G}]$ is actually the orthogonal projection of the random vari... | Intuition for Conditional Expectation of $\sigma$-algebra | One way to think about conditional expectation is as a projection onto the $\sigma$-algebra $\mathscr{G}$.
(from Wikimedia commons)
This is actually rigorously true when talking about square-integrab | Intuition for Conditional Expectation of $\sigma$-algebra
One way to think about conditional expectation is as a projection onto the $\sigma$-algebra $\mathscr{G}$.
(from Wikimedia commons)
This is actually rigorously true when talking about square-integrable random variables; in this case $\mathbb{E}[\xi|\mathscr{G}]... | Intuition for Conditional Expectation of $\sigma$-algebra
One way to think about conditional expectation is as a projection onto the $\sigma$-algebra $\mathscr{G}$.
(from Wikimedia commons)
This is actually rigorously true when talking about square-integrab |
7,185 | Intuition for Conditional Expectation of $\sigma$-algebra | I am going to try to elaborate what William suggested.
Let $\Omega$ be the sample space of tossing a coin twice. Define the ran. var. $\xi$ to be the num. of heads that occur in the experiment. Clearly, $E[\xi] = 1$. One way of thinking of what $1$, as an expec. value, represents is as the best possible estimate for $... | Intuition for Conditional Expectation of $\sigma$-algebra | I am going to try to elaborate what William suggested.
Let $\Omega$ be the sample space of tossing a coin twice. Define the ran. var. $\xi$ to be the num. of heads that occur in the experiment. Clear | Intuition for Conditional Expectation of $\sigma$-algebra
I am going to try to elaborate what William suggested.
Let $\Omega$ be the sample space of tossing a coin twice. Define the ran. var. $\xi$ to be the num. of heads that occur in the experiment. Clearly, $E[\xi] = 1$. One way of thinking of what $1$, as an expec... | Intuition for Conditional Expectation of $\sigma$-algebra
I am going to try to elaborate what William suggested.
Let $\Omega$ be the sample space of tossing a coin twice. Define the ran. var. $\xi$ to be the num. of heads that occur in the experiment. Clear |
7,186 | Intuition for Conditional Expectation of $\sigma$-algebra | Although you request not to use the formal definition, I think that the formal definition is probably the best way of explaining it.
Wikipedia - conditional expectation:
Then a conditional expectation of X given $\displaystyle \scriptstyle
{\mathcal {H}}$, denoted as $\displaystyle \scriptstyle \operatorname
{E} (X\... | Intuition for Conditional Expectation of $\sigma$-algebra | Although you request not to use the formal definition, I think that the formal definition is probably the best way of explaining it.
Wikipedia - conditional expectation:
Then a conditional expectatio | Intuition for Conditional Expectation of $\sigma$-algebra
Although you request not to use the formal definition, I think that the formal definition is probably the best way of explaining it.
Wikipedia - conditional expectation:
Then a conditional expectation of X given $\displaystyle \scriptstyle
{\mathcal {H}}$, den... | Intuition for Conditional Expectation of $\sigma$-algebra
Although you request not to use the formal definition, I think that the formal definition is probably the best way of explaining it.
Wikipedia - conditional expectation:
Then a conditional expectatio |
7,187 | Intuition for Conditional Expectation of $\sigma$-algebra | To help a little bit more, case ii in your question is a special case that a sigma-algebra being conditioned on is generated by a partition. I am sure you will find that case intuitive as well. | Intuition for Conditional Expectation of $\sigma$-algebra | To help a little bit more, case ii in your question is a special case that a sigma-algebra being conditioned on is generated by a partition. I am sure you will find that case intuitive as well. | Intuition for Conditional Expectation of $\sigma$-algebra
To help a little bit more, case ii in your question is a special case that a sigma-algebra being conditioned on is generated by a partition. I am sure you will find that case intuitive as well. | Intuition for Conditional Expectation of $\sigma$-algebra
To help a little bit more, case ii in your question is a special case that a sigma-algebra being conditioned on is generated by a partition. I am sure you will find that case intuitive as well. |
7,188 | How to calculate pooled variance of two or more groups given known group variances, means, and sample sizes? | The idea is to express quantities as sums rather than fractions.
Given any $n$ data values $x_i,$ use the definitions of the mean
$$\mu_{1:n} = \frac{1}{\Omega_{1;n}}\sum_{i=1}^n \omega_{i} x_i$$
and sample variance
$$\sigma_{1:n}^2 = \frac{1}{\Omega_{1;n}}\sum_{i=1}^n \omega_{i}\left(x_i - \mu_{1:n}\right)^2 = \frac{1... | How to calculate pooled variance of two or more groups given known group variances, means, and sampl | The idea is to express quantities as sums rather than fractions.
Given any $n$ data values $x_i,$ use the definitions of the mean
$$\mu_{1:n} = \frac{1}{\Omega_{1;n}}\sum_{i=1}^n \omega_{i} x_i$$
and | How to calculate pooled variance of two or more groups given known group variances, means, and sample sizes?
The idea is to express quantities as sums rather than fractions.
Given any $n$ data values $x_i,$ use the definitions of the mean
$$\mu_{1:n} = \frac{1}{\Omega_{1;n}}\sum_{i=1}^n \omega_{i} x_i$$
and sample vari... | How to calculate pooled variance of two or more groups given known group variances, means, and sampl
The idea is to express quantities as sums rather than fractions.
Given any $n$ data values $x_i,$ use the definitions of the mean
$$\mu_{1:n} = \frac{1}{\Omega_{1;n}}\sum_{i=1}^n \omega_{i} x_i$$
and |
7,189 | How to calculate pooled variance of two or more groups given known group variances, means, and sample sizes? | I'm going to use standard notation for sample means and sample variances in this answer, rather than the notation used in the question. Using standard notation, another formula for the pooled sample variance of two groups can be found in O'Neill (2014) (Result 1):
$$\begin{equation} \begin{aligned}
s_\text{pooled}^2 &... | How to calculate pooled variance of two or more groups given known group variances, means, and sampl | I'm going to use standard notation for sample means and sample variances in this answer, rather than the notation used in the question. Using standard notation, another formula for the pooled sample | How to calculate pooled variance of two or more groups given known group variances, means, and sample sizes?
I'm going to use standard notation for sample means and sample variances in this answer, rather than the notation used in the question. Using standard notation, another formula for the pooled sample variance of... | How to calculate pooled variance of two or more groups given known group variances, means, and sampl
I'm going to use standard notation for sample means and sample variances in this answer, rather than the notation used in the question. Using standard notation, another formula for the pooled sample |
7,190 | How to calculate pooled variance of two or more groups given known group variances, means, and sample sizes? | Use the sample.decomp function in the utilities package
Statistical problems of this kind have now been automated in the sample.decomp function in the utilities package. This function can compute pooled sample moments from subgroup moments, or compute missing subgroup moments from the other subgroup moments and pooled... | How to calculate pooled variance of two or more groups given known group variances, means, and sampl | Use the sample.decomp function in the utilities package
Statistical problems of this kind have now been automated in the sample.decomp function in the utilities package. This function can compute poo | How to calculate pooled variance of two or more groups given known group variances, means, and sample sizes?
Use the sample.decomp function in the utilities package
Statistical problems of this kind have now been automated in the sample.decomp function in the utilities package. This function can compute pooled sample ... | How to calculate pooled variance of two or more groups given known group variances, means, and sampl
Use the sample.decomp function in the utilities package
Statistical problems of this kind have now been automated in the sample.decomp function in the utilities package. This function can compute poo |
7,191 | How to fit an ARIMAX-model with R? | You're going to have a little bit of trouble modeling a series with 2 levels of seasonality using an ARIMA model. Getting this right is going highly dependent on setting things up correctly. Have you considered a simple linear model yet? They're a lot faster and easier to fit than ARIMA models, and if you use dummy ... | How to fit an ARIMAX-model with R? | You're going to have a little bit of trouble modeling a series with 2 levels of seasonality using an ARIMA model. Getting this right is going highly dependent on setting things up correctly. Have yo | How to fit an ARIMAX-model with R?
You're going to have a little bit of trouble modeling a series with 2 levels of seasonality using an ARIMA model. Getting this right is going highly dependent on setting things up correctly. Have you considered a simple linear model yet? They're a lot faster and easier to fit than ... | How to fit an ARIMAX-model with R?
You're going to have a little bit of trouble modeling a series with 2 levels of seasonality using an ARIMA model. Getting this right is going highly dependent on setting things up correctly. Have yo |
7,192 | How to fit an ARIMAX-model with R? | I've been using R to do load forecasting for a while and I can suggest you to use forecast package and its invaluable functions (like auto.arima).
You can build an ARIMA model with the following command:
model = arima(y, order, xreg = exogenous_data)
with y your predictand (I suppose dayy), order the order of your mo... | How to fit an ARIMAX-model with R? | I've been using R to do load forecasting for a while and I can suggest you to use forecast package and its invaluable functions (like auto.arima).
You can build an ARIMA model with the following comm | How to fit an ARIMAX-model with R?
I've been using R to do load forecasting for a while and I can suggest you to use forecast package and its invaluable functions (like auto.arima).
You can build an ARIMA model with the following command:
model = arima(y, order, xreg = exogenous_data)
with y your predictand (I suppos... | How to fit an ARIMAX-model with R?
I've been using R to do load forecasting for a while and I can suggest you to use forecast package and its invaluable functions (like auto.arima).
You can build an ARIMA model with the following comm |
7,193 | How to fit an ARIMAX-model with R? | I personally don't understand transfer functions, but I think you got the xtransf and xreg reversed. At least in R's base arima it is xreg that contains your exogenous variables. It's my impression that a transfer function describes how (lagged data affects future values) rather than what.
I'd try using xreg for your e... | How to fit an ARIMAX-model with R? | I personally don't understand transfer functions, but I think you got the xtransf and xreg reversed. At least in R's base arima it is xreg that contains your exogenous variables. It's my impression th | How to fit an ARIMAX-model with R?
I personally don't understand transfer functions, but I think you got the xtransf and xreg reversed. At least in R's base arima it is xreg that contains your exogenous variables. It's my impression that a transfer function describes how (lagged data affects future values) rather than ... | How to fit an ARIMAX-model with R?
I personally don't understand transfer functions, but I think you got the xtransf and xreg reversed. At least in R's base arima it is xreg that contains your exogenous variables. It's my impression th |
7,194 | When is logistic regression solved in closed form? | As kjetil b halvorsen pointed out, it is, in its own way, a miracle that linear regression admits an analytical solution. And this is so only by virtue of the linearity of the problem (with respect to the parameters). In OLS, you have
$$
\sum_i (y_i - x_i \beta)^2 \to \min_\beta,
$$
which has the first order conditions... | When is logistic regression solved in closed form? | As kjetil b halvorsen pointed out, it is, in its own way, a miracle that linear regression admits an analytical solution. And this is so only by virtue of the linearity of the problem (with respect to | When is logistic regression solved in closed form?
As kjetil b halvorsen pointed out, it is, in its own way, a miracle that linear regression admits an analytical solution. And this is so only by virtue of the linearity of the problem (with respect to the parameters). In OLS, you have
$$
\sum_i (y_i - x_i \beta)^2 \to ... | When is logistic regression solved in closed form?
As kjetil b halvorsen pointed out, it is, in its own way, a miracle that linear regression admits an analytical solution. And this is so only by virtue of the linearity of the problem (with respect to |
7,195 | When is logistic regression solved in closed form? | This post was originally intended as a long comment rather than a complete answer to the question at hand.
From the question, it's a little unclear if the interest lies only in the binary case or, perhaps, in more general cases where they may be continuous or take on other discrete values.
One example that doesn't quit... | When is logistic regression solved in closed form? | This post was originally intended as a long comment rather than a complete answer to the question at hand.
From the question, it's a little unclear if the interest lies only in the binary case or, per | When is logistic regression solved in closed form?
This post was originally intended as a long comment rather than a complete answer to the question at hand.
From the question, it's a little unclear if the interest lies only in the binary case or, perhaps, in more general cases where they may be continuous or take on o... | When is logistic regression solved in closed form?
This post was originally intended as a long comment rather than a complete answer to the question at hand.
From the question, it's a little unclear if the interest lies only in the binary case or, per |
7,196 | Why is using squared error the standard when absolute error is more relevant to most problems? [duplicate] | The first 5 answers fail to distinguish between estimation loss1 and prediction loss2, something that is crucial in answering the question. A priori, there is no reason that the two should coincide. I will discuss both types of loss in the context of point prediction using linear regression. The discussion can be exten... | Why is using squared error the standard when absolute error is more relevant to most problems? [dupl | The first 5 answers fail to distinguish between estimation loss1 and prediction loss2, something that is crucial in answering the question. A priori, there is no reason that the two should coincide. I | Why is using squared error the standard when absolute error is more relevant to most problems? [duplicate]
The first 5 answers fail to distinguish between estimation loss1 and prediction loss2, something that is crucial in answering the question. A priori, there is no reason that the two should coincide. I will discuss... | Why is using squared error the standard when absolute error is more relevant to most problems? [dupl
The first 5 answers fail to distinguish between estimation loss1 and prediction loss2, something that is crucial in answering the question. A priori, there is no reason that the two should coincide. I |
7,197 | Why is using squared error the standard when absolute error is more relevant to most problems? [duplicate] | TLDR; when nothing is known about actual cost of error to the user of the model, MSE is a better default option compared to MAE because, in my opinion, it is easier to manipulate analytically and is more likely to match the actual cost of error.
It's a great question. I like that you start with desire to make your loss... | Why is using squared error the standard when absolute error is more relevant to most problems? [dupl | TLDR; when nothing is known about actual cost of error to the user of the model, MSE is a better default option compared to MAE because, in my opinion, it is easier to manipulate analytically and is m | Why is using squared error the standard when absolute error is more relevant to most problems? [duplicate]
TLDR; when nothing is known about actual cost of error to the user of the model, MSE is a better default option compared to MAE because, in my opinion, it is easier to manipulate analytically and is more likely to... | Why is using squared error the standard when absolute error is more relevant to most problems? [dupl
TLDR; when nothing is known about actual cost of error to the user of the model, MSE is a better default option compared to MAE because, in my opinion, it is easier to manipulate analytically and is m |
7,198 | Why is using squared error the standard when absolute error is more relevant to most problems? [duplicate] | I think the reason is more sociological that statistical.
Short version: We do it this way because we always have.
Longer version:
Historically, we could not do many of the things we now take for granted. Many things are computer intensive and Ronald Fisher was born before Alan Turing.
So, people did OLS regression - ... | Why is using squared error the standard when absolute error is more relevant to most problems? [dupl | I think the reason is more sociological that statistical.
Short version: We do it this way because we always have.
Longer version:
Historically, we could not do many of the things we now take for gran | Why is using squared error the standard when absolute error is more relevant to most problems? [duplicate]
I think the reason is more sociological that statistical.
Short version: We do it this way because we always have.
Longer version:
Historically, we could not do many of the things we now take for granted. Many thi... | Why is using squared error the standard when absolute error is more relevant to most problems? [dupl
I think the reason is more sociological that statistical.
Short version: We do it this way because we always have.
Longer version:
Historically, we could not do many of the things we now take for gran |
7,199 | Why is using squared error the standard when absolute error is more relevant to most problems? [duplicate] | I think it's worth taking a step back and considering what the two losses imply.
Looking at it from a probabilistic point of view, the loss function is equivalent to the assumed log-likelihood function and thus should correspond to how we think our measurements are distributed around their unknown 'true' values.
As you... | Why is using squared error the standard when absolute error is more relevant to most problems? [dupl | I think it's worth taking a step back and considering what the two losses imply.
Looking at it from a probabilistic point of view, the loss function is equivalent to the assumed log-likelihood functio | Why is using squared error the standard when absolute error is more relevant to most problems? [duplicate]
I think it's worth taking a step back and considering what the two losses imply.
Looking at it from a probabilistic point of view, the loss function is equivalent to the assumed log-likelihood function and thus sh... | Why is using squared error the standard when absolute error is more relevant to most problems? [dupl
I think it's worth taking a step back and considering what the two losses imply.
Looking at it from a probabilistic point of view, the loss function is equivalent to the assumed log-likelihood functio |
7,200 | Why is using squared error the standard when absolute error is more relevant to most problems? [duplicate] | If errors are independent and follow the normal distribution (of any variance but consistent), then the sum of squared errors corresponds to their joint probability/likelihood.
$\Pi e^{-x_i^2}=e^{-\Sigma x_i^2}$
So under those conditions minimizing the sum of square errors is the same as maximizing the likelihood.
If ... | Why is using squared error the standard when absolute error is more relevant to most problems? [dupl | If errors are independent and follow the normal distribution (of any variance but consistent), then the sum of squared errors corresponds to their joint probability/likelihood.
$\Pi e^{-x_i^2}=e^{-\Si | Why is using squared error the standard when absolute error is more relevant to most problems? [duplicate]
If errors are independent and follow the normal distribution (of any variance but consistent), then the sum of squared errors corresponds to their joint probability/likelihood.
$\Pi e^{-x_i^2}=e^{-\Sigma x_i^2}$
S... | Why is using squared error the standard when absolute error is more relevant to most problems? [dupl
If errors are independent and follow the normal distribution (of any variance but consistent), then the sum of squared errors corresponds to their joint probability/likelihood.
$\Pi e^{-x_i^2}=e^{-\Si |
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