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 |
|---|---|---|---|---|---|---|
501 | What are common statistical sins? | Requesting, and perhaps obtaining The Flow Chart: That graphical thing where you say what the level of your variables are and what sort of relationship you're looking for, and you follow the arrows down to get a Brand Name Test or a Brand Name Statistic. Sometimes offered with mysterious 'parametric' and 'non-parametr... | What are common statistical sins? | Requesting, and perhaps obtaining The Flow Chart: That graphical thing where you say what the level of your variables are and what sort of relationship you're looking for, and you follow the arrows do | What are common statistical sins?
Requesting, and perhaps obtaining The Flow Chart: That graphical thing where you say what the level of your variables are and what sort of relationship you're looking for, and you follow the arrows down to get a Brand Name Test or a Brand Name Statistic. Sometimes offered with mysteri... | What are common statistical sins?
Requesting, and perhaps obtaining The Flow Chart: That graphical thing where you say what the level of your variables are and what sort of relationship you're looking for, and you follow the arrows do |
502 | What are common statistical sins? | Using pie charts to illustrate relative frequencies. More here. | What are common statistical sins? | Using pie charts to illustrate relative frequencies. More here. | What are common statistical sins?
Using pie charts to illustrate relative frequencies. More here. | What are common statistical sins?
Using pie charts to illustrate relative frequencies. More here. |
503 | What are common statistical sins? | Using statistics/probability in hypothesis testing to measure the "absolute truth". Statistics simply cannot do this, they can only be of use in deciding between alternatives, which must be specified from "outside" the statistical paradigm. Statements such as "the null hypothesis is proved true by the statistics" are... | What are common statistical sins? | Using statistics/probability in hypothesis testing to measure the "absolute truth". Statistics simply cannot do this, they can only be of use in deciding between alternatives, which must be specified | What are common statistical sins?
Using statistics/probability in hypothesis testing to measure the "absolute truth". Statistics simply cannot do this, they can only be of use in deciding between alternatives, which must be specified from "outside" the statistical paradigm. Statements such as "the null hypothesis is ... | What are common statistical sins?
Using statistics/probability in hypothesis testing to measure the "absolute truth". Statistics simply cannot do this, they can only be of use in deciding between alternatives, which must be specified |
504 | What are common statistical sins? | Repeating the same or similar experiment over 20 times on the same data and then reporting a statistically significant result with $\alpha = 0.05$. Incidentally there is a comic about this one.
And similarly to (or almost the same as) @ogrisel's answer, performing a Grid search and reporting only the best result. | What are common statistical sins? | Repeating the same or similar experiment over 20 times on the same data and then reporting a statistically significant result with $\alpha = 0.05$. Incidentally there is a comic about this one.
And s | What are common statistical sins?
Repeating the same or similar experiment over 20 times on the same data and then reporting a statistically significant result with $\alpha = 0.05$. Incidentally there is a comic about this one.
And similarly to (or almost the same as) @ogrisel's answer, performing a Grid search and re... | What are common statistical sins?
Repeating the same or similar experiment over 20 times on the same data and then reporting a statistically significant result with $\alpha = 0.05$. Incidentally there is a comic about this one.
And s |
505 | What are common statistical sins? | (With a bit of luck this will be controversial.)
Using a Neyman-Pearson approach to statistical analysis of scientific experiments. Or, worse, using an ill-defined hybrid of Neyman-Pearson and Fisher. | What are common statistical sins? | (With a bit of luck this will be controversial.)
Using a Neyman-Pearson approach to statistical analysis of scientific experiments. Or, worse, using an ill-defined hybrid of Neyman-Pearson and Fisher. | What are common statistical sins?
(With a bit of luck this will be controversial.)
Using a Neyman-Pearson approach to statistical analysis of scientific experiments. Or, worse, using an ill-defined hybrid of Neyman-Pearson and Fisher. | What are common statistical sins?
(With a bit of luck this will be controversial.)
Using a Neyman-Pearson approach to statistical analysis of scientific experiments. Or, worse, using an ill-defined hybrid of Neyman-Pearson and Fisher. |
506 | ROC vs precision-and-recall curves | The key difference is that ROC curves will be the same no matter what the baseline probability is, but PR curves may be more useful in practice for needle-in-haystack type problems or problems where the "positive" class is more interesting than the negative class.
To show this, first let's start with a very nice way to... | ROC vs precision-and-recall curves | The key difference is that ROC curves will be the same no matter what the baseline probability is, but PR curves may be more useful in practice for needle-in-haystack type problems or problems where t | ROC vs precision-and-recall curves
The key difference is that ROC curves will be the same no matter what the baseline probability is, but PR curves may be more useful in practice for needle-in-haystack type problems or problems where the "positive" class is more interesting than the negative class.
To show this, first ... | ROC vs precision-and-recall curves
The key difference is that ROC curves will be the same no matter what the baseline probability is, but PR curves may be more useful in practice for needle-in-haystack type problems or problems where t |
507 | ROC vs precision-and-recall curves | Here are the conclusions from a paper by Davis & Goadrich explaining the relationship between ROC and PR space. They answer the first two questions:
First, for any dataset, the ROC curve and PR curve for a given algorithm contain the same points. This equivalence, leads to the surprising theorem that a curve dominates... | ROC vs precision-and-recall curves | Here are the conclusions from a paper by Davis & Goadrich explaining the relationship between ROC and PR space. They answer the first two questions:
First, for any dataset, the ROC curve and PR curve | ROC vs precision-and-recall curves
Here are the conclusions from a paper by Davis & Goadrich explaining the relationship between ROC and PR space. They answer the first two questions:
First, for any dataset, the ROC curve and PR curve for a given algorithm contain the same points. This equivalence, leads to the surpri... | ROC vs precision-and-recall curves
Here are the conclusions from a paper by Davis & Goadrich explaining the relationship between ROC and PR space. They answer the first two questions:
First, for any dataset, the ROC curve and PR curve |
508 | ROC vs precision-and-recall curves | There is a lot of misunderstanding about evaluation. Part of this comes from the Machine Learning approach of trying to optimize algorithms on datasets, with no real interest in the data.
In a medical context, it's about the real world outcomes - how many people you save from dying, for example. In a medical context S... | ROC vs precision-and-recall curves | There is a lot of misunderstanding about evaluation. Part of this comes from the Machine Learning approach of trying to optimize algorithms on datasets, with no real interest in the data.
In a medica | ROC vs precision-and-recall curves
There is a lot of misunderstanding about evaluation. Part of this comes from the Machine Learning approach of trying to optimize algorithms on datasets, with no real interest in the data.
In a medical context, it's about the real world outcomes - how many people you save from dying, ... | ROC vs precision-and-recall curves
There is a lot of misunderstanding about evaluation. Part of this comes from the Machine Learning approach of trying to optimize algorithms on datasets, with no real interest in the data.
In a medica |
509 | ROC vs precision-and-recall curves | TL;DR
$AUC_{PvR}$ highlights the amount of False Positives relative to the class size, whereas $AUC_{ROC}$ better reflects the total amount of False Positives independent of in which class they come up.
Definition
The $AUC_{ROC}$ (receiver operator) is the area under the curve of true positive to false positive rate.
T... | ROC vs precision-and-recall curves | TL;DR
$AUC_{PvR}$ highlights the amount of False Positives relative to the class size, whereas $AUC_{ROC}$ better reflects the total amount of False Positives independent of in which class they come u | ROC vs precision-and-recall curves
TL;DR
$AUC_{PvR}$ highlights the amount of False Positives relative to the class size, whereas $AUC_{ROC}$ better reflects the total amount of False Positives independent of in which class they come up.
Definition
The $AUC_{ROC}$ (receiver operator) is the area under the curve of true... | ROC vs precision-and-recall curves
TL;DR
$AUC_{PvR}$ highlights the amount of False Positives relative to the class size, whereas $AUC_{ROC}$ better reflects the total amount of False Positives independent of in which class they come u |
510 | What exactly are keys, queries, and values in attention mechanisms? | The key/value/query formulation of attention is from the paper Attention Is All You Need.
How should one understand the queries, keys, and values
The key/value/query concept is analogous to retrieval systems. For example, when you search for videos on Youtube, the search engine will map your query (text in the search... | What exactly are keys, queries, and values in attention mechanisms? | The key/value/query formulation of attention is from the paper Attention Is All You Need.
How should one understand the queries, keys, and values
The key/value/query concept is analogous to retrieva | What exactly are keys, queries, and values in attention mechanisms?
The key/value/query formulation of attention is from the paper Attention Is All You Need.
How should one understand the queries, keys, and values
The key/value/query concept is analogous to retrieval systems. For example, when you search for videos o... | What exactly are keys, queries, and values in attention mechanisms?
The key/value/query formulation of attention is from the paper Attention Is All You Need.
How should one understand the queries, keys, and values
The key/value/query concept is analogous to retrieva |
511 | What exactly are keys, queries, and values in attention mechanisms? | I was also puzzled by the keys, queries, and values in the attention mechanisms for a while. After searching on the Web and digesting relevant information, I have a clear picture about how the keys, queries, and values work and why they would work!
Let's see how they work, followed by why they work.
Attention to repla... | What exactly are keys, queries, and values in attention mechanisms? | I was also puzzled by the keys, queries, and values in the attention mechanisms for a while. After searching on the Web and digesting relevant information, I have a clear picture about how the keys, | What exactly are keys, queries, and values in attention mechanisms?
I was also puzzled by the keys, queries, and values in the attention mechanisms for a while. After searching on the Web and digesting relevant information, I have a clear picture about how the keys, queries, and values work and why they would work!
Le... | What exactly are keys, queries, and values in attention mechanisms?
I was also puzzled by the keys, queries, and values in the attention mechanisms for a while. After searching on the Web and digesting relevant information, I have a clear picture about how the keys, |
512 | What exactly are keys, queries, and values in attention mechanisms? | First, understand Q and K
First, focus on the objective of First MatMul in the Scaled dot product attention using Q and K.
Intuition on what is Attention
For the sentence "jane visits africa".
When your eyes see jane, your brain looks for the most related word in the rest of the sentence to understand what jane is abo... | What exactly are keys, queries, and values in attention mechanisms? | First, understand Q and K
First, focus on the objective of First MatMul in the Scaled dot product attention using Q and K.
Intuition on what is Attention
For the sentence "jane visits africa".
When y | What exactly are keys, queries, and values in attention mechanisms?
First, understand Q and K
First, focus on the objective of First MatMul in the Scaled dot product attention using Q and K.
Intuition on what is Attention
For the sentence "jane visits africa".
When your eyes see jane, your brain looks for the most rel... | What exactly are keys, queries, and values in attention mechanisms?
First, understand Q and K
First, focus on the objective of First MatMul in the Scaled dot product attention using Q and K.
Intuition on what is Attention
For the sentence "jane visits africa".
When y |
513 | What exactly are keys, queries, and values in attention mechanisms? | See Attention is all you need - masterclass, from 15:46 onwards Lukasz Kaiser explains what q, K and V are.
So basically:
q = the vector representing a word
K and V = your memory, thus all the words that have been generated before. Note that K and V can be the same (but don't have to).
So what you do with attention ... | What exactly are keys, queries, and values in attention mechanisms? | See Attention is all you need - masterclass, from 15:46 onwards Lukasz Kaiser explains what q, K and V are.
So basically:
q = the vector representing a word
K and V = your memory, thus all the words | What exactly are keys, queries, and values in attention mechanisms?
See Attention is all you need - masterclass, from 15:46 onwards Lukasz Kaiser explains what q, K and V are.
So basically:
q = the vector representing a word
K and V = your memory, thus all the words that have been generated before. Note that K and V ... | What exactly are keys, queries, and values in attention mechanisms?
See Attention is all you need - masterclass, from 15:46 onwards Lukasz Kaiser explains what q, K and V are.
So basically:
q = the vector representing a word
K and V = your memory, thus all the words |
514 | What exactly are keys, queries, and values in attention mechanisms? | I'm going to try provide an English text example. The following is based solely on my intuitive understanding of the paper 'Attention is all you need'.
Say you have a sentence:
I like Natural Language Processing , a lot !
Assume that we already have input word vectors for all the 9 tokens in the previous sentence. So... | What exactly are keys, queries, and values in attention mechanisms? | I'm going to try provide an English text example. The following is based solely on my intuitive understanding of the paper 'Attention is all you need'.
Say you have a sentence:
I like Natural Languag | What exactly are keys, queries, and values in attention mechanisms?
I'm going to try provide an English text example. The following is based solely on my intuitive understanding of the paper 'Attention is all you need'.
Say you have a sentence:
I like Natural Language Processing , a lot !
Assume that we already have ... | What exactly are keys, queries, and values in attention mechanisms?
I'm going to try provide an English text example. The following is based solely on my intuitive understanding of the paper 'Attention is all you need'.
Say you have a sentence:
I like Natural Languag |
515 | What exactly are keys, queries, and values in attention mechanisms? | Tensorflow and Keras just expanded on their documentation for the Attention and AdditiveAttention layers. Here is a sneaky peek from the docs:
The meaning of query, value and key depend on the application. In the case of text similarity, for example, query is the sequence embeddings of the first piece of text and val... | What exactly are keys, queries, and values in attention mechanisms? | Tensorflow and Keras just expanded on their documentation for the Attention and AdditiveAttention layers. Here is a sneaky peek from the docs:
The meaning of query, value and key depend on the appli | What exactly are keys, queries, and values in attention mechanisms?
Tensorflow and Keras just expanded on their documentation for the Attention and AdditiveAttention layers. Here is a sneaky peek from the docs:
The meaning of query, value and key depend on the application. In the case of text similarity, for example,... | What exactly are keys, queries, and values in attention mechanisms?
Tensorflow and Keras just expanded on their documentation for the Attention and AdditiveAttention layers. Here is a sneaky peek from the docs:
The meaning of query, value and key depend on the appli |
516 | What exactly are keys, queries, and values in attention mechanisms? | Queries is a set of vectors you want to calculate attention for.
Keys is a set of vectors you want to calculate attention against.
As a result of dot product multiplication you'll get set of weights a (also vectors) showing how attended each query against Keys.
Then you multiply it by Values to get resulting set of vec... | What exactly are keys, queries, and values in attention mechanisms? | Queries is a set of vectors you want to calculate attention for.
Keys is a set of vectors you want to calculate attention against.
As a result of dot product multiplication you'll get set of weights a | What exactly are keys, queries, and values in attention mechanisms?
Queries is a set of vectors you want to calculate attention for.
Keys is a set of vectors you want to calculate attention against.
As a result of dot product multiplication you'll get set of weights a (also vectors) showing how attended each query agai... | What exactly are keys, queries, and values in attention mechanisms?
Queries is a set of vectors you want to calculate attention for.
Keys is a set of vectors you want to calculate attention against.
As a result of dot product multiplication you'll get set of weights a |
517 | What exactly are keys, queries, and values in attention mechanisms? | Where are people getting the key, query, and value from these
equations?
The paper you refer to does not use such terminology as "key", "query", or "value", so it is not clear what you mean in here. There is no single definition of "attention" for neural networks, so my guess is that you confused two definitions fro... | What exactly are keys, queries, and values in attention mechanisms? | Where are people getting the key, query, and value from these
equations?
The paper you refer to does not use such terminology as "key", "query", or "value", so it is not clear what you mean in here | What exactly are keys, queries, and values in attention mechanisms?
Where are people getting the key, query, and value from these
equations?
The paper you refer to does not use such terminology as "key", "query", or "value", so it is not clear what you mean in here. There is no single definition of "attention" for n... | What exactly are keys, queries, and values in attention mechanisms?
Where are people getting the key, query, and value from these
equations?
The paper you refer to does not use such terminology as "key", "query", or "value", so it is not clear what you mean in here |
518 | What exactly are keys, queries, and values in attention mechanisms? | This is an add up of what is K and V and why the author use different parameter to represent K and V. Short answer is technically K and V can be different and there is a case where people use different values for K and V.
K and V can be different! Example Offered!
What are K and V? Are they the same?
The short answer i... | What exactly are keys, queries, and values in attention mechanisms? | This is an add up of what is K and V and why the author use different parameter to represent K and V. Short answer is technically K and V can be different and there is a case where people use differen | What exactly are keys, queries, and values in attention mechanisms?
This is an add up of what is K and V and why the author use different parameter to represent K and V. Short answer is technically K and V can be different and there is a case where people use different values for K and V.
K and V can be different! Exam... | What exactly are keys, queries, and values in attention mechanisms?
This is an add up of what is K and V and why the author use different parameter to represent K and V. Short answer is technically K and V can be different and there is a case where people use differen |
519 | Why is Newton's method not widely used in machine learning? | Gradient descent maximizes a function using knowledge of its derivative. Newton's method, a root finding algorithm, maximizes a function using knowledge of its second derivative. That can be faster when the second derivative is known and easy to compute (the Newton-Raphson algorithm is used in logistic regression). How... | Why is Newton's method not widely used in machine learning? | Gradient descent maximizes a function using knowledge of its derivative. Newton's method, a root finding algorithm, maximizes a function using knowledge of its second derivative. That can be faster wh | Why is Newton's method not widely used in machine learning?
Gradient descent maximizes a function using knowledge of its derivative. Newton's method, a root finding algorithm, maximizes a function using knowledge of its second derivative. That can be faster when the second derivative is known and easy to compute (the N... | Why is Newton's method not widely used in machine learning?
Gradient descent maximizes a function using knowledge of its derivative. Newton's method, a root finding algorithm, maximizes a function using knowledge of its second derivative. That can be faster wh |
520 | Why is Newton's method not widely used in machine learning? | More people should be using Newton's method in machine learning*. I say this as someone with a background in numerical optimization, who has dabbled in machine learning over the past couple of years.
The drawbacks in answers here (and even in the literature) are not an issue if you use Newton's method correctly. Moreov... | Why is Newton's method not widely used in machine learning? | More people should be using Newton's method in machine learning*. I say this as someone with a background in numerical optimization, who has dabbled in machine learning over the past couple of years.
| Why is Newton's method not widely used in machine learning?
More people should be using Newton's method in machine learning*. I say this as someone with a background in numerical optimization, who has dabbled in machine learning over the past couple of years.
The drawbacks in answers here (and even in the literature) a... | Why is Newton's method not widely used in machine learning?
More people should be using Newton's method in machine learning*. I say this as someone with a background in numerical optimization, who has dabbled in machine learning over the past couple of years.
|
521 | Why is Newton's method not widely used in machine learning? | A combination of two reasons:
Newton method attracts to saddle points;
saddle points are common in machine learning, or in fact any multivariable optimization.
Look at the function $$f=x^2-y^2$$
If you apply multivariate Newton method, you get the following.
$$\mathbf{x}_{n+1} = \mathbf{x}_n - [\mathbf{H}f(\mathbf... | Why is Newton's method not widely used in machine learning? | A combination of two reasons:
Newton method attracts to saddle points;
saddle points are common in machine learning, or in fact any multivariable optimization.
Look at the function $$f=x^2-y^2$$
| Why is Newton's method not widely used in machine learning?
A combination of two reasons:
Newton method attracts to saddle points;
saddle points are common in machine learning, or in fact any multivariable optimization.
Look at the function $$f=x^2-y^2$$
If you apply multivariate Newton method, you get the followi... | Why is Newton's method not widely used in machine learning?
A combination of two reasons:
Newton method attracts to saddle points;
saddle points are common in machine learning, or in fact any multivariable optimization.
Look at the function $$f=x^2-y^2$$
|
522 | Why is Newton's method not widely used in machine learning? | I recently learned this myself - the problem is the proliferation of saddle points in high-dimensional space, that Newton methods want to converge to. See this article: Identifying and attacking the saddle point problem in
high-dimensional non-convex optimization.
Indeed the ratio of the number of saddle points to loc... | Why is Newton's method not widely used in machine learning? | I recently learned this myself - the problem is the proliferation of saddle points in high-dimensional space, that Newton methods want to converge to. See this article: Identifying and attacking the s | Why is Newton's method not widely used in machine learning?
I recently learned this myself - the problem is the proliferation of saddle points in high-dimensional space, that Newton methods want to converge to. See this article: Identifying and attacking the saddle point problem in
high-dimensional non-convex optimizat... | Why is Newton's method not widely used in machine learning?
I recently learned this myself - the problem is the proliferation of saddle points in high-dimensional space, that Newton methods want to converge to. See this article: Identifying and attacking the s |
523 | Why is Newton's method not widely used in machine learning? | You asked two questions: Why don't more people use Newton's method, and why do so many
people use stochastic gradient descent? These questions have different answers, because
there are many algorithms that lessen the computational burden of Newton's method
but often work better than SGD.
First: Newton's Method takes a ... | Why is Newton's method not widely used in machine learning? | You asked two questions: Why don't more people use Newton's method, and why do so many
people use stochastic gradient descent? These questions have different answers, because
there are many algorithms | Why is Newton's method not widely used in machine learning?
You asked two questions: Why don't more people use Newton's method, and why do so many
people use stochastic gradient descent? These questions have different answers, because
there are many algorithms that lessen the computational burden of Newton's method
but... | Why is Newton's method not widely used in machine learning?
You asked two questions: Why don't more people use Newton's method, and why do so many
people use stochastic gradient descent? These questions have different answers, because
there are many algorithms |
524 | Why is Newton's method not widely used in machine learning? | Gradient descent direction's cheaper to calculate, and performing a line search in that direction is a more reliable, steady source of progress toward an optimum. In short, gradient descent's relatively reliable.
Newton's method is relatively expensive in that you need to calculate the Hessian on the first iteration. ... | Why is Newton's method not widely used in machine learning? | Gradient descent direction's cheaper to calculate, and performing a line search in that direction is a more reliable, steady source of progress toward an optimum. In short, gradient descent's relativ | Why is Newton's method not widely used in machine learning?
Gradient descent direction's cheaper to calculate, and performing a line search in that direction is a more reliable, steady source of progress toward an optimum. In short, gradient descent's relatively reliable.
Newton's method is relatively expensive in tha... | Why is Newton's method not widely used in machine learning?
Gradient descent direction's cheaper to calculate, and performing a line search in that direction is a more reliable, steady source of progress toward an optimum. In short, gradient descent's relativ |
525 | Why is Newton's method not widely used in machine learning? | For large dimensions, the Hessian is typically expensive to store and solving
$Hd = g$ for a direction can be expensive. It is also more difficult to parallelise.
Newton's method works well when close to a solution, or if the Hessian is
slowly varying, but needs some tricks to deal with lack of convergence and
lack of... | Why is Newton's method not widely used in machine learning? | For large dimensions, the Hessian is typically expensive to store and solving
$Hd = g$ for a direction can be expensive. It is also more difficult to parallelise.
Newton's method works well when clos | Why is Newton's method not widely used in machine learning?
For large dimensions, the Hessian is typically expensive to store and solving
$Hd = g$ for a direction can be expensive. It is also more difficult to parallelise.
Newton's method works well when close to a solution, or if the Hessian is
slowly varying, but ne... | Why is Newton's method not widely used in machine learning?
For large dimensions, the Hessian is typically expensive to store and solving
$Hd = g$ for a direction can be expensive. It is also more difficult to parallelise.
Newton's method works well when clos |
526 | Why is Newton's method not widely used in machine learning? | There are many difficulties regarding the use of Newton's method for SGD, especially:
it requires to know local Hessian matrix - how to estimate Hessian e.g. from noisy gradients with a sufficient precision at a reasonable cost?
full Hessian is too costly - we rather need some its restriction, e.g. to a linear subspa... | Why is Newton's method not widely used in machine learning? | There are many difficulties regarding the use of Newton's method for SGD, especially:
it requires to know local Hessian matrix - how to estimate Hessian e.g. from noisy gradients with a sufficient pr | Why is Newton's method not widely used in machine learning?
There are many difficulties regarding the use of Newton's method for SGD, especially:
it requires to know local Hessian matrix - how to estimate Hessian e.g. from noisy gradients with a sufficient precision at a reasonable cost?
full Hessian is too costly - ... | Why is Newton's method not widely used in machine learning?
There are many difficulties regarding the use of Newton's method for SGD, especially:
it requires to know local Hessian matrix - how to estimate Hessian e.g. from noisy gradients with a sufficient pr |
527 | Why is Newton's method not widely used in machine learning? | Just some comments:
First order methods have very well theoretical guarantee about convergence and avoidance of saddle points, see Backtracking GD and modifications.
Backtracking GD can be implemented in DNN, with very good performance.
Backtracking GD allows big learning rates, can be of the size of inverse of the si... | Why is Newton's method not widely used in machine learning? | Just some comments:
First order methods have very well theoretical guarantee about convergence and avoidance of saddle points, see Backtracking GD and modifications.
Backtracking GD can be implemente | Why is Newton's method not widely used in machine learning?
Just some comments:
First order methods have very well theoretical guarantee about convergence and avoidance of saddle points, see Backtracking GD and modifications.
Backtracking GD can be implemented in DNN, with very good performance.
Backtracking GD allows... | Why is Newton's method not widely used in machine learning?
Just some comments:
First order methods have very well theoretical guarantee about convergence and avoidance of saddle points, see Backtracking GD and modifications.
Backtracking GD can be implemente |
528 | What is the best introductory Bayesian statistics textbook? | John Kruschke released a book in mid 2011 called Doing Bayesian Data Analysis: A Tutorial with R and BUGS. (A second edition was released in Nov 2014: Doing Bayesian Data Analysis, Second Edition: A Tutorial with R, JAGS, and Stan.) It is truly introductory. If you want to walk from frequentist stats into Bayes though,... | What is the best introductory Bayesian statistics textbook? | John Kruschke released a book in mid 2011 called Doing Bayesian Data Analysis: A Tutorial with R and BUGS. (A second edition was released in Nov 2014: Doing Bayesian Data Analysis, Second Edition: A T | What is the best introductory Bayesian statistics textbook?
John Kruschke released a book in mid 2011 called Doing Bayesian Data Analysis: A Tutorial with R and BUGS. (A second edition was released in Nov 2014: Doing Bayesian Data Analysis, Second Edition: A Tutorial with R, JAGS, and Stan.) It is truly introductory. I... | What is the best introductory Bayesian statistics textbook?
John Kruschke released a book in mid 2011 called Doing Bayesian Data Analysis: A Tutorial with R and BUGS. (A second edition was released in Nov 2014: Doing Bayesian Data Analysis, Second Edition: A T |
529 | What is the best introductory Bayesian statistics textbook? | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
My favorite is "Bayesian Data Analysis" by Gelman, et ... | What is the best introductory Bayesian statistics textbook? | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
| What is the best introductory Bayesian statistics textbook?
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
... | What is the best introductory Bayesian statistics textbook?
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
|
530 | What is the best introductory Bayesian statistics textbook? | Statistical Rethinking, has been released just a few weeks ago and hence I am still reading it, but I think is a very nice and fresh addition to the really introductory books about Bayesian Statistics. The author uses a similar approach as the one used by John Kruschke in his puppy books; very verbose, detailed explana... | What is the best introductory Bayesian statistics textbook? | Statistical Rethinking, has been released just a few weeks ago and hence I am still reading it, but I think is a very nice and fresh addition to the really introductory books about Bayesian Statistics | What is the best introductory Bayesian statistics textbook?
Statistical Rethinking, has been released just a few weeks ago and hence I am still reading it, but I think is a very nice and fresh addition to the really introductory books about Bayesian Statistics. The author uses a similar approach as the one used by John... | What is the best introductory Bayesian statistics textbook?
Statistical Rethinking, has been released just a few weeks ago and hence I am still reading it, but I think is a very nice and fresh addition to the really introductory books about Bayesian Statistics |
531 | What is the best introductory Bayesian statistics textbook? | Sivia and Skilling, Data analysis: a Bayesian tutorial (2ed) 2006 246p 0198568320
books.goo:
Statistics lectures have been a source
of much bewilderment and frustration
for generations of students. This book
attempts to remedy the situation by
expounding a logical and unified
approach to the whole subject of... | What is the best introductory Bayesian statistics textbook? | Sivia and Skilling, Data analysis: a Bayesian tutorial (2ed) 2006 246p 0198568320
books.goo:
Statistics lectures have been a source
of much bewilderment and frustration
for generations of student | What is the best introductory Bayesian statistics textbook?
Sivia and Skilling, Data analysis: a Bayesian tutorial (2ed) 2006 246p 0198568320
books.goo:
Statistics lectures have been a source
of much bewilderment and frustration
for generations of students. This book
attempts to remedy the situation by
expound... | What is the best introductory Bayesian statistics textbook?
Sivia and Skilling, Data analysis: a Bayesian tutorial (2ed) 2006 246p 0198568320
books.goo:
Statistics lectures have been a source
of much bewilderment and frustration
for generations of student |
532 | What is the best introductory Bayesian statistics textbook? | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
Another vote for Gelman et al., but a close second for... | What is the best introductory Bayesian statistics textbook? | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
| What is the best introductory Bayesian statistics textbook?
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
... | What is the best introductory Bayesian statistics textbook?
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
|
533 | What is the best introductory Bayesian statistics textbook? | For an introduction, I would recommend Probabilistic Programming & Bayesian Methods for Hackers by Cam Davidson-Pilon, freely available online.
From its description:
An intro to Bayesian methods and probabilistic programming from a computation/understanding-first, mathematics-second point of view.
It's highly visual,... | What is the best introductory Bayesian statistics textbook? | For an introduction, I would recommend Probabilistic Programming & Bayesian Methods for Hackers by Cam Davidson-Pilon, freely available online.
From its description:
An intro to Bayesian methods and | What is the best introductory Bayesian statistics textbook?
For an introduction, I would recommend Probabilistic Programming & Bayesian Methods for Hackers by Cam Davidson-Pilon, freely available online.
From its description:
An intro to Bayesian methods and probabilistic programming from a computation/understanding-f... | What is the best introductory Bayesian statistics textbook?
For an introduction, I would recommend Probabilistic Programming & Bayesian Methods for Hackers by Cam Davidson-Pilon, freely available online.
From its description:
An intro to Bayesian methods and |
534 | What is the best introductory Bayesian statistics textbook? | I thoroughly recommend the entertaining polemic "Probability Theory: The Logic of Science" by E.T. Jaynes.
This is an introductory text in the sense of not requiring (and in fact preferring) no previous knowledge of statistics, but it does eventually employ fairly sophisticated mathematics. Compared to most of the oth... | What is the best introductory Bayesian statistics textbook? | I thoroughly recommend the entertaining polemic "Probability Theory: The Logic of Science" by E.T. Jaynes.
This is an introductory text in the sense of not requiring (and in fact preferring) no previ | What is the best introductory Bayesian statistics textbook?
I thoroughly recommend the entertaining polemic "Probability Theory: The Logic of Science" by E.T. Jaynes.
This is an introductory text in the sense of not requiring (and in fact preferring) no previous knowledge of statistics, but it does eventually employ f... | What is the best introductory Bayesian statistics textbook?
I thoroughly recommend the entertaining polemic "Probability Theory: The Logic of Science" by E.T. Jaynes.
This is an introductory text in the sense of not requiring (and in fact preferring) no previ |
535 | What is the best introductory Bayesian statistics textbook? | Its focus isn't strictly on Bayesian statistics, so it lacks some methodology, but David MacKay's Information Theory, Inference, and Learning Algorithms made me intuitively grasp Bayesian statistics better than others - most do the how quite nicely, but I felt MacKay explained why better. | What is the best introductory Bayesian statistics textbook? | Its focus isn't strictly on Bayesian statistics, so it lacks some methodology, but David MacKay's Information Theory, Inference, and Learning Algorithms made me intuitively grasp Bayesian statistics b | What is the best introductory Bayesian statistics textbook?
Its focus isn't strictly on Bayesian statistics, so it lacks some methodology, but David MacKay's Information Theory, Inference, and Learning Algorithms made me intuitively grasp Bayesian statistics better than others - most do the how quite nicely, but I felt... | What is the best introductory Bayesian statistics textbook?
Its focus isn't strictly on Bayesian statistics, so it lacks some methodology, but David MacKay's Information Theory, Inference, and Learning Algorithms made me intuitively grasp Bayesian statistics b |
536 | What is the best introductory Bayesian statistics textbook? | I am an electrical engineer and not a statistician. I spent a lot of time to go through Gelman but I don't think one can refer to Gelman as introductory at all. My bayesian-guru professor from Carnegie Mellon agrees with me on this. having the minimum knowledge of statistics and R and Bugs(as the easy way to DO somethi... | What is the best introductory Bayesian statistics textbook? | I am an electrical engineer and not a statistician. I spent a lot of time to go through Gelman but I don't think one can refer to Gelman as introductory at all. My bayesian-guru professor from Carnegi | What is the best introductory Bayesian statistics textbook?
I am an electrical engineer and not a statistician. I spent a lot of time to go through Gelman but I don't think one can refer to Gelman as introductory at all. My bayesian-guru professor from Carnegie Mellon agrees with me on this. having the minimum knowledg... | What is the best introductory Bayesian statistics textbook?
I am an electrical engineer and not a statistician. I spent a lot of time to go through Gelman but I don't think one can refer to Gelman as introductory at all. My bayesian-guru professor from Carnegi |
537 | What is the best introductory Bayesian statistics textbook? | The Gelman books are all excellent but not necessarily introductory in that they assume that you know some statistics already. Therefore they are an introduction to the Bayesian way of doing statistics rather than to statistics in general. I would still give them the thumbs up, however.
As an introductory statistics/ec... | What is the best introductory Bayesian statistics textbook? | The Gelman books are all excellent but not necessarily introductory in that they assume that you know some statistics already. Therefore they are an introduction to the Bayesian way of doing statistic | What is the best introductory Bayesian statistics textbook?
The Gelman books are all excellent but not necessarily introductory in that they assume that you know some statistics already. Therefore they are an introduction to the Bayesian way of doing statistics rather than to statistics in general. I would still give t... | What is the best introductory Bayesian statistics textbook?
The Gelman books are all excellent but not necessarily introductory in that they assume that you know some statistics already. Therefore they are an introduction to the Bayesian way of doing statistic |
538 | What is the best introductory Bayesian statistics textbook? | I don't know why nobody has mentioned the very introductory book on Bayesian:
There's a free PDF version for the book. The book offers enough material for anyone who has very little experience in bayesian. It introduces the concept of prior distribution, posterior distribution, beta distribution etc.
Give it a go, it'... | What is the best introductory Bayesian statistics textbook? | I don't know why nobody has mentioned the very introductory book on Bayesian:
There's a free PDF version for the book. The book offers enough material for anyone who has very little experience in bay | What is the best introductory Bayesian statistics textbook?
I don't know why nobody has mentioned the very introductory book on Bayesian:
There's a free PDF version for the book. The book offers enough material for anyone who has very little experience in bayesian. It introduces the concept of prior distribution, post... | What is the best introductory Bayesian statistics textbook?
I don't know why nobody has mentioned the very introductory book on Bayesian:
There's a free PDF version for the book. The book offers enough material for anyone who has very little experience in bay |
539 | What is the best introductory Bayesian statistics textbook? | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
"Bayesian Core: A Practical Approach to Computational ... | What is the best introductory Bayesian statistics textbook? | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
| What is the best introductory Bayesian statistics textbook?
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
... | What is the best introductory Bayesian statistics textbook?
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
|
540 | What is the best introductory Bayesian statistics textbook? | My favourite first undergraduate text for bayesian statistics is by Bolstad, Introduction to Bayesian Statistics. If you're looking for something graduate level, this will be too elementary, but for someone who is new to statistics this is ideal. | What is the best introductory Bayesian statistics textbook? | My favourite first undergraduate text for bayesian statistics is by Bolstad, Introduction to Bayesian Statistics. If you're looking for something graduate level, this will be too elementary, but for | What is the best introductory Bayesian statistics textbook?
My favourite first undergraduate text for bayesian statistics is by Bolstad, Introduction to Bayesian Statistics. If you're looking for something graduate level, this will be too elementary, but for someone who is new to statistics this is ideal. | What is the best introductory Bayesian statistics textbook?
My favourite first undergraduate text for bayesian statistics is by Bolstad, Introduction to Bayesian Statistics. If you're looking for something graduate level, this will be too elementary, but for |
541 | What is the best introductory Bayesian statistics textbook? | I have read some parts of A First Course in Bayesian Statistical Methods by Peter Hoff, and I found it easy to follow. (Example R-code is provided throughout the text) | What is the best introductory Bayesian statistics textbook? | I have read some parts of A First Course in Bayesian Statistical Methods by Peter Hoff, and I found it easy to follow. (Example R-code is provided throughout the text) | What is the best introductory Bayesian statistics textbook?
I have read some parts of A First Course in Bayesian Statistical Methods by Peter Hoff, and I found it easy to follow. (Example R-code is provided throughout the text) | What is the best introductory Bayesian statistics textbook?
I have read some parts of A First Course in Bayesian Statistical Methods by Peter Hoff, and I found it easy to follow. (Example R-code is provided throughout the text) |
542 | What is the best introductory Bayesian statistics textbook? | Coming from non-statistical background I found Introduction to Applied Bayesian Statistics and Estimation for Social Scientists quite informative and easy to follow. | What is the best introductory Bayesian statistics textbook? | Coming from non-statistical background I found Introduction to Applied Bayesian Statistics and Estimation for Social Scientists quite informative and easy to follow. | What is the best introductory Bayesian statistics textbook?
Coming from non-statistical background I found Introduction to Applied Bayesian Statistics and Estimation for Social Scientists quite informative and easy to follow. | What is the best introductory Bayesian statistics textbook?
Coming from non-statistical background I found Introduction to Applied Bayesian Statistics and Estimation for Social Scientists quite informative and easy to follow. |
543 | What is the best introductory Bayesian statistics textbook? | If you're looking for an elementary text, i.e. one that doesn't have a calculus prerequisite, there's Don Berry's Statistics: A Bayesian Perspective. | What is the best introductory Bayesian statistics textbook? | If you're looking for an elementary text, i.e. one that doesn't have a calculus prerequisite, there's Don Berry's Statistics: A Bayesian Perspective. | What is the best introductory Bayesian statistics textbook?
If you're looking for an elementary text, i.e. one that doesn't have a calculus prerequisite, there's Don Berry's Statistics: A Bayesian Perspective. | What is the best introductory Bayesian statistics textbook?
If you're looking for an elementary text, i.e. one that doesn't have a calculus prerequisite, there's Don Berry's Statistics: A Bayesian Perspective. |
544 | What is the best introductory Bayesian statistics textbook? | I found an excellent introduction in Gelman and Hill (2007) Data Analysis Using Regression and Multilevel/Hierarchical Models. (Other comments mention it, but it deserves to get upvoted on its own.) | What is the best introductory Bayesian statistics textbook? | I found an excellent introduction in Gelman and Hill (2007) Data Analysis Using Regression and Multilevel/Hierarchical Models. (Other comments mention it, but it deserves to get upvoted on its own.) | What is the best introductory Bayesian statistics textbook?
I found an excellent introduction in Gelman and Hill (2007) Data Analysis Using Regression and Multilevel/Hierarchical Models. (Other comments mention it, but it deserves to get upvoted on its own.) | What is the best introductory Bayesian statistics textbook?
I found an excellent introduction in Gelman and Hill (2007) Data Analysis Using Regression and Multilevel/Hierarchical Models. (Other comments mention it, but it deserves to get upvoted on its own.) |
545 | What is the best introductory Bayesian statistics textbook? | Take a look at "The Bayesian Choice". It has the full package: foundations, applications and computation. Clearly written. | What is the best introductory Bayesian statistics textbook? | Take a look at "The Bayesian Choice". It has the full package: foundations, applications and computation. Clearly written. | What is the best introductory Bayesian statistics textbook?
Take a look at "The Bayesian Choice". It has the full package: foundations, applications and computation. Clearly written. | What is the best introductory Bayesian statistics textbook?
Take a look at "The Bayesian Choice". It has the full package: foundations, applications and computation. Clearly written. |
546 | What is the best introductory Bayesian statistics textbook? | I've at least glanced at most of these on this list and none are as good as the new Bayesian Ideas and Data Analysis in my opinion.
Edit: It is easy to immediately begin doing Bayesian analysis while reading this book. Not just model the mean from a Normal distribution with known variance, but actual data analysis aft... | What is the best introductory Bayesian statistics textbook? | I've at least glanced at most of these on this list and none are as good as the new Bayesian Ideas and Data Analysis in my opinion.
Edit: It is easy to immediately begin doing Bayesian analysis while | What is the best introductory Bayesian statistics textbook?
I've at least glanced at most of these on this list and none are as good as the new Bayesian Ideas and Data Analysis in my opinion.
Edit: It is easy to immediately begin doing Bayesian analysis while reading this book. Not just model the mean from a Normal di... | What is the best introductory Bayesian statistics textbook?
I've at least glanced at most of these on this list and none are as good as the new Bayesian Ideas and Data Analysis in my opinion.
Edit: It is easy to immediately begin doing Bayesian analysis while |
547 | What is the best introductory Bayesian statistics textbook? | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
I quite like Markov Chain Monte Carlo: Stochastic Simu... | What is the best introductory Bayesian statistics textbook? | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
| What is the best introductory Bayesian statistics textbook?
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
... | What is the best introductory Bayesian statistics textbook?
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
|
548 | What is the best introductory Bayesian statistics textbook? | If I had to choose a single text for a beginner, it would be
Sivia DS and Skilling J (2006) book (see below).
Of all the books listed below it strives hardest to give an intuitive grasp of the essential ideas, but it still requires some mathematical sophistication from page 1.
Below is a list of Furthe... | What is the best introductory Bayesian statistics textbook? | If I had to choose a single text for a beginner, it would be
Sivia DS and Skilling J (2006) book (see below).
Of all the books listed below it strives hardest to give an intuitive gra | What is the best introductory Bayesian statistics textbook?
If I had to choose a single text for a beginner, it would be
Sivia DS and Skilling J (2006) book (see below).
Of all the books listed below it strives hardest to give an intuitive grasp of the essential ideas, but it still requires some mathem... | What is the best introductory Bayesian statistics textbook?
If I had to choose a single text for a beginner, it would be
Sivia DS and Skilling J (2006) book (see below).
Of all the books listed below it strives hardest to give an intuitive gra |
549 | What is the best introductory Bayesian statistics textbook? | For complete beginners, try William Briggs Breaking the Law of Averages: Real-Life Probability and Statistics in Plain English | What is the best introductory Bayesian statistics textbook? | For complete beginners, try William Briggs Breaking the Law of Averages: Real-Life Probability and Statistics in Plain English | What is the best introductory Bayesian statistics textbook?
For complete beginners, try William Briggs Breaking the Law of Averages: Real-Life Probability and Statistics in Plain English | What is the best introductory Bayesian statistics textbook?
For complete beginners, try William Briggs Breaking the Law of Averages: Real-Life Probability and Statistics in Plain English |
550 | What is the best introductory Bayesian statistics textbook? | I simply must to include MCMC in Practice. It provides an excellent introduction to MCMC, perhaps not as general as other books mentioned, but excellent for gaining insight and intuition. I would recommend reading it after (or in parallel with) Bayesian Computation with R. | What is the best introductory Bayesian statistics textbook? | I simply must to include MCMC in Practice. It provides an excellent introduction to MCMC, perhaps not as general as other books mentioned, but excellent for gaining insight and intuition. I would re | What is the best introductory Bayesian statistics textbook?
I simply must to include MCMC in Practice. It provides an excellent introduction to MCMC, perhaps not as general as other books mentioned, but excellent for gaining insight and intuition. I would recommend reading it after (or in parallel with) Bayesian Comp... | What is the best introductory Bayesian statistics textbook?
I simply must to include MCMC in Practice. It provides an excellent introduction to MCMC, perhaps not as general as other books mentioned, but excellent for gaining insight and intuition. I would re |
551 | What is the best introductory Bayesian statistics textbook? | If you happen to come from the physical sciencies (physics/astronomy) I would recommend you Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with Mathematica® Support by Gregory (2006).
Although the "with Mathematica® Support" part of the title is there only for commercial issues (the us... | What is the best introductory Bayesian statistics textbook? | If you happen to come from the physical sciencies (physics/astronomy) I would recommend you Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with Mathematica® Support b | What is the best introductory Bayesian statistics textbook?
If you happen to come from the physical sciencies (physics/astronomy) I would recommend you Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with Mathematica® Support by Gregory (2006).
Although the "with Mathematica® Support" p... | What is the best introductory Bayesian statistics textbook?
If you happen to come from the physical sciencies (physics/astronomy) I would recommend you Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with Mathematica® Support b |
552 | What is the best introductory Bayesian statistics textbook? | I read:
Gelman et al (2013). Bayesian Data Analysis. CRC Press LLC. 3rd ed.
Hoff, Peter D (2009). A First Course in Bayesian Statistical Methods. Springer Texts in Statistics.
Kruschke, Doing Bayesian Data Analysis: A Tutorial with R and Bugs, 2011. Academic Press / Elsevier.
and I think that the better one to start wi... | What is the best introductory Bayesian statistics textbook? | I read:
Gelman et al (2013). Bayesian Data Analysis. CRC Press LLC. 3rd ed.
Hoff, Peter D (2009). A First Course in Bayesian Statistical Methods. Springer Texts in Statistics.
Kruschke, Doing Bayesian | What is the best introductory Bayesian statistics textbook?
I read:
Gelman et al (2013). Bayesian Data Analysis. CRC Press LLC. 3rd ed.
Hoff, Peter D (2009). A First Course in Bayesian Statistical Methods. Springer Texts in Statistics.
Kruschke, Doing Bayesian Data Analysis: A Tutorial with R and Bugs, 2011. Academic P... | What is the best introductory Bayesian statistics textbook?
I read:
Gelman et al (2013). Bayesian Data Analysis. CRC Press LLC. 3rd ed.
Hoff, Peter D (2009). A First Course in Bayesian Statistical Methods. Springer Texts in Statistics.
Kruschke, Doing Bayesian |
553 | What is the best introductory Bayesian statistics textbook? | Not strictly Bayesian Statistics as such, but I can strongly recommend "A First Course on Machine Learning" by Rogers and Girolami, which is essentially an introduction to Bayesian approaches to machine learning. Its very well structured and clear and aimed at students without a strong mathematical background. This m... | What is the best introductory Bayesian statistics textbook? | Not strictly Bayesian Statistics as such, but I can strongly recommend "A First Course on Machine Learning" by Rogers and Girolami, which is essentially an introduction to Bayesian approaches to machi | What is the best introductory Bayesian statistics textbook?
Not strictly Bayesian Statistics as such, but I can strongly recommend "A First Course on Machine Learning" by Rogers and Girolami, which is essentially an introduction to Bayesian approaches to machine learning. Its very well structured and clear and aimed a... | What is the best introductory Bayesian statistics textbook?
Not strictly Bayesian Statistics as such, but I can strongly recommend "A First Course on Machine Learning" by Rogers and Girolami, which is essentially an introduction to Bayesian approaches to machi |
554 | What is the best introductory Bayesian statistics textbook? | Bayesian Statistics for Social Scientists. Phillips, Lawrence D. (1973), Thomas Crowell & Co. It's very clear, very accessible, assumes no statistics knowledge, and, unlike Bolstad which I found dry, has some personality. | What is the best introductory Bayesian statistics textbook? | Bayesian Statistics for Social Scientists. Phillips, Lawrence D. (1973), Thomas Crowell & Co. It's very clear, very accessible, assumes no statistics knowledge, and, unlike Bolstad which I found dry, | What is the best introductory Bayesian statistics textbook?
Bayesian Statistics for Social Scientists. Phillips, Lawrence D. (1973), Thomas Crowell & Co. It's very clear, very accessible, assumes no statistics knowledge, and, unlike Bolstad which I found dry, has some personality. | What is the best introductory Bayesian statistics textbook?
Bayesian Statistics for Social Scientists. Phillips, Lawrence D. (1973), Thomas Crowell & Co. It's very clear, very accessible, assumes no statistics knowledge, and, unlike Bolstad which I found dry, |
555 | What is the best introductory Bayesian statistics textbook? | This book suggests it is aimed at entry level undergraduate level
Biostatistics: A Bayesian Introduction. By George G Woodsworth.
Published by John Wiley & Sons | What is the best introductory Bayesian statistics textbook? | This book suggests it is aimed at entry level undergraduate level
Biostatistics: A Bayesian Introduction. By George G Woodsworth.
Published by John Wiley & Sons | What is the best introductory Bayesian statistics textbook?
This book suggests it is aimed at entry level undergraduate level
Biostatistics: A Bayesian Introduction. By George G Woodsworth.
Published by John Wiley & Sons | What is the best introductory Bayesian statistics textbook?
This book suggests it is aimed at entry level undergraduate level
Biostatistics: A Bayesian Introduction. By George G Woodsworth.
Published by John Wiley & Sons |
556 | What is the best introductory Bayesian statistics textbook? | Computational Bayesian Statistics by Turkman et. al. is a high-quality and all-inclusive introduction to Bayesian statistics and its computational aspects. It has the right mix of theory, model assessment and selection, and a dedicated chapter on software for Bayesian statistics (with code examples). It should serve ni... | What is the best introductory Bayesian statistics textbook? | Computational Bayesian Statistics by Turkman et. al. is a high-quality and all-inclusive introduction to Bayesian statistics and its computational aspects. It has the right mix of theory, model assess | What is the best introductory Bayesian statistics textbook?
Computational Bayesian Statistics by Turkman et. al. is a high-quality and all-inclusive introduction to Bayesian statistics and its computational aspects. It has the right mix of theory, model assessment and selection, and a dedicated chapter on software for ... | What is the best introductory Bayesian statistics textbook?
Computational Bayesian Statistics by Turkman et. al. is a high-quality and all-inclusive introduction to Bayesian statistics and its computational aspects. It has the right mix of theory, model assess |
557 | What is the best introductory Bayesian statistics textbook? | A good book from the basics to advanced, and which you can download, is
Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin, Bayesian Data Analysis, http://www.stat.columbia.edu/~gelman/book/
You can also download the first two chapters of
Richard McElreath, A Bayesian Course with Example... | What is the best introductory Bayesian statistics textbook? | A good book from the basics to advanced, and which you can download, is
Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin, Bayesian Data Analysis, http://www.stat.colu | What is the best introductory Bayesian statistics textbook?
A good book from the basics to advanced, and which you can download, is
Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin, Bayesian Data Analysis, http://www.stat.columbia.edu/~gelman/book/
You can also download the first two c... | What is the best introductory Bayesian statistics textbook?
A good book from the basics to advanced, and which you can download, is
Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin, Bayesian Data Analysis, http://www.stat.colu |
558 | Algorithms for automatic model selection | I think this approach is mistaken, but perhaps it will be more helpful if I explain why. Wanting to know the best model given some information about a large number of variables is quite understandable. Moreover, it is a situation in which people seem to find themselves regularly. In addition, many textbooks (and cou... | Algorithms for automatic model selection | I think this approach is mistaken, but perhaps it will be more helpful if I explain why. Wanting to know the best model given some information about a large number of variables is quite understandabl | Algorithms for automatic model selection
I think this approach is mistaken, but perhaps it will be more helpful if I explain why. Wanting to know the best model given some information about a large number of variables is quite understandable. Moreover, it is a situation in which people seem to find themselves regular... | Algorithms for automatic model selection
I think this approach is mistaken, but perhaps it will be more helpful if I explain why. Wanting to know the best model given some information about a large number of variables is quite understandabl |
559 | Algorithms for automatic model selection | Check out the caret package in R. It will help you cross-validate step-wise regression models (use method='lmStepAIC' or method='glmStepAIC'), and might help you understand how these sorts of models tend to have poor predictive performance. Furthermore, you can use the findCorrelation function in caret to identify an... | Algorithms for automatic model selection | Check out the caret package in R. It will help you cross-validate step-wise regression models (use method='lmStepAIC' or method='glmStepAIC'), and might help you understand how these sorts of models | Algorithms for automatic model selection
Check out the caret package in R. It will help you cross-validate step-wise regression models (use method='lmStepAIC' or method='glmStepAIC'), and might help you understand how these sorts of models tend to have poor predictive performance. Furthermore, you can use the findCor... | Algorithms for automatic model selection
Check out the caret package in R. It will help you cross-validate step-wise regression models (use method='lmStepAIC' or method='glmStepAIC'), and might help you understand how these sorts of models |
560 | Algorithms for automatic model selection | I fully concur with the problems outlined by @gung. That said, realistically speaking, model selection is a real problem in need of a real solution. Here's something I would use in practice.
Split your data into training, validation, and test sets.
Train models on your training set.
Measure model performance on the va... | Algorithms for automatic model selection | I fully concur with the problems outlined by @gung. That said, realistically speaking, model selection is a real problem in need of a real solution. Here's something I would use in practice.
Split yo | Algorithms for automatic model selection
I fully concur with the problems outlined by @gung. That said, realistically speaking, model selection is a real problem in need of a real solution. Here's something I would use in practice.
Split your data into training, validation, and test sets.
Train models on your training... | Algorithms for automatic model selection
I fully concur with the problems outlined by @gung. That said, realistically speaking, model selection is a real problem in need of a real solution. Here's something I would use in practice.
Split yo |
561 | Algorithms for automatic model selection | To answer the question, there are several options:
all-subset by AIC/BIC
stepwise by p-value
stepwise by AIC/BIC
regularisation such as LASSO (can be based on either AIC/BIC or CV)
genetic algorithm (GA)
others?
use of non-automatic, theory ("subject matter knowledge") oriented selection
Next question would b... | Algorithms for automatic model selection | To answer the question, there are several options:
all-subset by AIC/BIC
stepwise by p-value
stepwise by AIC/BIC
regularisation such as LASSO (can be based on either AIC/BIC or CV)
genetic algor | Algorithms for automatic model selection
To answer the question, there are several options:
all-subset by AIC/BIC
stepwise by p-value
stepwise by AIC/BIC
regularisation such as LASSO (can be based on either AIC/BIC or CV)
genetic algorithm (GA)
others?
use of non-automatic, theory ("subject matter knowledge") o... | Algorithms for automatic model selection
To answer the question, there are several options:
all-subset by AIC/BIC
stepwise by p-value
stepwise by AIC/BIC
regularisation such as LASSO (can be based on either AIC/BIC or CV)
genetic algor |
562 | Algorithms for automatic model selection | Here's an answer out of left field- instead of using linear regression, use a regression tree (rpart package). This is suitable for automatic model selection because with a little work you can automate the selection of cp, the parameter used to avoid over-fitting. | Algorithms for automatic model selection | Here's an answer out of left field- instead of using linear regression, use a regression tree (rpart package). This is suitable for automatic model selection because with a little work you can automat | Algorithms for automatic model selection
Here's an answer out of left field- instead of using linear regression, use a regression tree (rpart package). This is suitable for automatic model selection because with a little work you can automate the selection of cp, the parameter used to avoid over-fitting. | Algorithms for automatic model selection
Here's an answer out of left field- instead of using linear regression, use a regression tree (rpart package). This is suitable for automatic model selection because with a little work you can automat |
563 | Algorithms for automatic model selection | linear model can be optimised by implementing genetic algorithm in the way of choosing most valuable independant variables. The variables are represented as genes in the algorithm, and the best chromosome (set of genes) are then being selected after crossover, mutation etc. operators. It is based on natural selection ... | Algorithms for automatic model selection | linear model can be optimised by implementing genetic algorithm in the way of choosing most valuable independant variables. The variables are represented as genes in the algorithm, and the best chromo | Algorithms for automatic model selection
linear model can be optimised by implementing genetic algorithm in the way of choosing most valuable independant variables. The variables are represented as genes in the algorithm, and the best chromosome (set of genes) are then being selected after crossover, mutation etc. ope... | Algorithms for automatic model selection
linear model can be optimised by implementing genetic algorithm in the way of choosing most valuable independant variables. The variables are represented as genes in the algorithm, and the best chromo |
564 | Algorithms for automatic model selection | Answers here advises against variable selection, but the problem is real ... and still done. One idea that should be tried out more in practice is blind analyses, as discussed in this nature paper Blind analysis: Hide results to seek the truth.
This idea has been mentioned in another post at this site, Multiple compar... | Algorithms for automatic model selection | Answers here advises against variable selection, but the problem is real ... and still done. One idea that should be tried out more in practice is blind analyses, as discussed in this nature paper Bli | Algorithms for automatic model selection
Answers here advises against variable selection, but the problem is real ... and still done. One idea that should be tried out more in practice is blind analyses, as discussed in this nature paper Blind analysis: Hide results to seek the truth.
This idea has been mentioned in a... | Algorithms for automatic model selection
Answers here advises against variable selection, but the problem is real ... and still done. One idea that should be tried out more in practice is blind analyses, as discussed in this nature paper Bli |
565 | Algorithms for automatic model selection | I see my question generated lots of interest and an interesting debate about the validity of the automatic model selection approach. While I agree that taking for granted the result of an automatic selection is risky, it can be used as a starting point. So here is how I implemented it for my particular problem, which i... | Algorithms for automatic model selection | I see my question generated lots of interest and an interesting debate about the validity of the automatic model selection approach. While I agree that taking for granted the result of an automatic se | Algorithms for automatic model selection
I see my question generated lots of interest and an interesting debate about the validity of the automatic model selection approach. While I agree that taking for granted the result of an automatic selection is risky, it can be used as a starting point. So here is how I implemen... | Algorithms for automatic model selection
I see my question generated lots of interest and an interesting debate about the validity of the automatic model selection approach. While I agree that taking for granted the result of an automatic se |
566 | When (and why) should you take the log of a distribution (of numbers)? | If you assume a model form that is non-linear but can be transformed to a linear model such as $\log Y = \beta_0 + \beta_1t$ then one would be justified in taking logarithms of $Y$ to meet the specified model form. In general whether or not you have causal series , the only time you would be justified or correct in tak... | When (and why) should you take the log of a distribution (of numbers)? | If you assume a model form that is non-linear but can be transformed to a linear model such as $\log Y = \beta_0 + \beta_1t$ then one would be justified in taking logarithms of $Y$ to meet the specifi | When (and why) should you take the log of a distribution (of numbers)?
If you assume a model form that is non-linear but can be transformed to a linear model such as $\log Y = \beta_0 + \beta_1t$ then one would be justified in taking logarithms of $Y$ to meet the specified model form. In general whether or not you have... | When (and why) should you take the log of a distribution (of numbers)?
If you assume a model form that is non-linear but can be transformed to a linear model such as $\log Y = \beta_0 + \beta_1t$ then one would be justified in taking logarithms of $Y$ to meet the specifi |
567 | When (and why) should you take the log of a distribution (of numbers)? | Log-scale informs on relative changes (multiplicative), while linear-scale informs on absolute changes (additive). When do you use each? When you care about relative changes, use the log-scale; when you care about absolute changes, use linear-scale. This is true for distributions, but also for any quantity or changes i... | When (and why) should you take the log of a distribution (of numbers)? | Log-scale informs on relative changes (multiplicative), while linear-scale informs on absolute changes (additive). When do you use each? When you care about relative changes, use the log-scale; when y | When (and why) should you take the log of a distribution (of numbers)?
Log-scale informs on relative changes (multiplicative), while linear-scale informs on absolute changes (additive). When do you use each? When you care about relative changes, use the log-scale; when you care about absolute changes, use linear-scale.... | When (and why) should you take the log of a distribution (of numbers)?
Log-scale informs on relative changes (multiplicative), while linear-scale informs on absolute changes (additive). When do you use each? When you care about relative changes, use the log-scale; when y |
568 | When (and why) should you take the log of a distribution (of numbers)? | I wanted to give an answer in the simplist form. If exponents are short hand for multiplication, and log is the inverse of exponentiation, the taking the log of something is a form of division.
Take the simplest function form y = C. Let C be 100,000 so we have y=100,000. If ws dona log() transform we have y=5.
If we... | When (and why) should you take the log of a distribution (of numbers)? | I wanted to give an answer in the simplist form. If exponents are short hand for multiplication, and log is the inverse of exponentiation, the taking the log of something is a form of division.
Take t | When (and why) should you take the log of a distribution (of numbers)?
I wanted to give an answer in the simplist form. If exponents are short hand for multiplication, and log is the inverse of exponentiation, the taking the log of something is a form of division.
Take the simplest function form y = C. Let C be 100,000... | When (and why) should you take the log of a distribution (of numbers)?
I wanted to give an answer in the simplist form. If exponents are short hand for multiplication, and log is the inverse of exponentiation, the taking the log of something is a form of division.
Take t |
569 | When (and why) should you take the log of a distribution (of numbers)? | A practical answer:
Why use log?
1.To avoid numerical underflow / overflow
In statistical inference or parameter learning processes, it's very common to cumulate product a series of probability densities. But some times the individual densities are too small (or too big) that computer won't be able to store their produ... | When (and why) should you take the log of a distribution (of numbers)? | A practical answer:
Why use log?
1.To avoid numerical underflow / overflow
In statistical inference or parameter learning processes, it's very common to cumulate product a series of probability densit | When (and why) should you take the log of a distribution (of numbers)?
A practical answer:
Why use log?
1.To avoid numerical underflow / overflow
In statistical inference or parameter learning processes, it's very common to cumulate product a series of probability densities. But some times the individual densities are ... | When (and why) should you take the log of a distribution (of numbers)?
A practical answer:
Why use log?
1.To avoid numerical underflow / overflow
In statistical inference or parameter learning processes, it's very common to cumulate product a series of probability densit |
570 | How to interpret a QQ plot | If the values lie along a line the distribution has the same shape (up to location and scale) as the theoretical distribution we have supposed.
Local behaviour: When looking at sorted sample values on the y-axis and (approximate) expected quantiles on the x-axis, we can identify from how the values in some section of ... | How to interpret a QQ plot | If the values lie along a line the distribution has the same shape (up to location and scale) as the theoretical distribution we have supposed.
Local behaviour: When looking at sorted sample values o | How to interpret a QQ plot
If the values lie along a line the distribution has the same shape (up to location and scale) as the theoretical distribution we have supposed.
Local behaviour: When looking at sorted sample values on the y-axis and (approximate) expected quantiles on the x-axis, we can identify from how the... | How to interpret a QQ plot
If the values lie along a line the distribution has the same shape (up to location and scale) as the theoretical distribution we have supposed.
Local behaviour: When looking at sorted sample values o |
571 | How to interpret a QQ plot | I made a shiny app to help interpret normal QQ plot. Try this link.
In this app, you can adjust the skewness, tailedness (kurtosis) and modality of data and you can see how the histogram and QQ plot change. Conversely, you can use it in a way that given the pattern of QQ plot, then check how the skewness etc should be.... | How to interpret a QQ plot | I made a shiny app to help interpret normal QQ plot. Try this link.
In this app, you can adjust the skewness, tailedness (kurtosis) and modality of data and you can see how the histogram and QQ plot c | How to interpret a QQ plot
I made a shiny app to help interpret normal QQ plot. Try this link.
In this app, you can adjust the skewness, tailedness (kurtosis) and modality of data and you can see how the histogram and QQ plot change. Conversely, you can use it in a way that given the pattern of QQ plot, then check how ... | How to interpret a QQ plot
I made a shiny app to help interpret normal QQ plot. Try this link.
In this app, you can adjust the skewness, tailedness (kurtosis) and modality of data and you can see how the histogram and QQ plot c |
572 | How to interpret a QQ plot | A very helpful (and intuitive) explanation is given by prof. Philippe Rigollet in the MIT MOOC course: 18.650 Statistics for Applications, Fall 2016 - see video at 45 mins
https://www.youtube.com/watch?v=vMaKx9fmJHE
I have crudely copied his diagram which I keep in my notes as I find it very useful.
In example 1, in ... | How to interpret a QQ plot | A very helpful (and intuitive) explanation is given by prof. Philippe Rigollet in the MIT MOOC course: 18.650 Statistics for Applications, Fall 2016 - see video at 45 mins
https://www.youtube.com/watc | How to interpret a QQ plot
A very helpful (and intuitive) explanation is given by prof. Philippe Rigollet in the MIT MOOC course: 18.650 Statistics for Applications, Fall 2016 - see video at 45 mins
https://www.youtube.com/watch?v=vMaKx9fmJHE
I have crudely copied his diagram which I keep in my notes as I find it very ... | How to interpret a QQ plot
A very helpful (and intuitive) explanation is given by prof. Philippe Rigollet in the MIT MOOC course: 18.650 Statistics for Applications, Fall 2016 - see video at 45 mins
https://www.youtube.com/watc |
573 | How to interpret a QQ plot | Since this thread has been deemed to be a definitive "how to interpret the normal q-q plot" StackExchange post, I would like to point readers to a nice, precise mathematical relationship between the normal q-q plot and the excess kurtosis statistic.
Here it is:
https://stats.stackexchange.com/a/354076/102879
A brief ... | How to interpret a QQ plot | Since this thread has been deemed to be a definitive "how to interpret the normal q-q plot" StackExchange post, I would like to point readers to a nice, precise mathematical relationship between the n | How to interpret a QQ plot
Since this thread has been deemed to be a definitive "how to interpret the normal q-q plot" StackExchange post, I would like to point readers to a nice, precise mathematical relationship between the normal q-q plot and the excess kurtosis statistic.
Here it is:
https://stats.stackexchange.c... | How to interpret a QQ plot
Since this thread has been deemed to be a definitive "how to interpret the normal q-q plot" StackExchange post, I would like to point readers to a nice, precise mathematical relationship between the n |
574 | How should I transform non-negative data including zeros? | It seems to me that the most appropriate choice of transformation is contingent on the model and the context.
The '0' point can arise from several different reasons each of which may have to be treated differently:
Truncation (as in Robin's example): Use appropriate models (e.g., mixtures, survival models etc)
Missin... | How should I transform non-negative data including zeros? | It seems to me that the most appropriate choice of transformation is contingent on the model and the context.
The '0' point can arise from several different reasons each of which may have to be treat | How should I transform non-negative data including zeros?
It seems to me that the most appropriate choice of transformation is contingent on the model and the context.
The '0' point can arise from several different reasons each of which may have to be treated differently:
Truncation (as in Robin's example): Use appro... | How should I transform non-negative data including zeros?
It seems to me that the most appropriate choice of transformation is contingent on the model and the context.
The '0' point can arise from several different reasons each of which may have to be treat |
575 | How should I transform non-negative data including zeros? | No-one mentioned the inverse hyperbolic sine transformation. So for completeness I'm adding it here.
This is an alternative to the Box-Cox transformations and is defined by
\begin{equation}
f(y,\theta) = \text{sinh}^{-1}(\theta y)/\theta = \log[\theta y + (\theta^2y^2+1)^{1/2}]/\theta,
\end{equation}
where $\theta>0$. ... | How should I transform non-negative data including zeros? | No-one mentioned the inverse hyperbolic sine transformation. So for completeness I'm adding it here.
This is an alternative to the Box-Cox transformations and is defined by
\begin{equation}
f(y,\theta | How should I transform non-negative data including zeros?
No-one mentioned the inverse hyperbolic sine transformation. So for completeness I'm adding it here.
This is an alternative to the Box-Cox transformations and is defined by
\begin{equation}
f(y,\theta) = \text{sinh}^{-1}(\theta y)/\theta = \log[\theta y + (\thet... | How should I transform non-negative data including zeros?
No-one mentioned the inverse hyperbolic sine transformation. So for completeness I'm adding it here.
This is an alternative to the Box-Cox transformations and is defined by
\begin{equation}
f(y,\theta |
576 | How should I transform non-negative data including zeros? | A useful approach when the variable is used as an independent factor in regression is to replace it by two variables: one is a binary indicator of whether it is zero and the other is the value of the original variable or a re-expression of it, such as its logarithm. This technique is discussed in Hosmer & Lemeshow's b... | How should I transform non-negative data including zeros? | A useful approach when the variable is used as an independent factor in regression is to replace it by two variables: one is a binary indicator of whether it is zero and the other is the value of the | How should I transform non-negative data including zeros?
A useful approach when the variable is used as an independent factor in regression is to replace it by two variables: one is a binary indicator of whether it is zero and the other is the value of the original variable or a re-expression of it, such as its logari... | How should I transform non-negative data including zeros?
A useful approach when the variable is used as an independent factor in regression is to replace it by two variables: one is a binary indicator of whether it is zero and the other is the value of the |
577 | How should I transform non-negative data including zeros? | The log transforms with shifts are special cases of the Box-Cox transformations:
$y(\lambda_{1}, \lambda_{2}) =
\begin{cases}
\frac {(y+\lambda_{2})^{\lambda_1} - 1} {\lambda_{1}} & \mbox{when } \lambda_{1} \neq 0 \\ \log (y + \lambda_{2}) & \mbox{when } \lambda_{1} = 0
\end{cases}$
These are the extended form for n... | How should I transform non-negative data including zeros? | The log transforms with shifts are special cases of the Box-Cox transformations:
$y(\lambda_{1}, \lambda_{2}) =
\begin{cases}
\frac {(y+\lambda_{2})^{\lambda_1} - 1} {\lambda_{1}} & \mbox{when } \l | How should I transform non-negative data including zeros?
The log transforms with shifts are special cases of the Box-Cox transformations:
$y(\lambda_{1}, \lambda_{2}) =
\begin{cases}
\frac {(y+\lambda_{2})^{\lambda_1} - 1} {\lambda_{1}} & \mbox{when } \lambda_{1} \neq 0 \\ \log (y + \lambda_{2}) & \mbox{when } \lam... | How should I transform non-negative data including zeros?
The log transforms with shifts are special cases of the Box-Cox transformations:
$y(\lambda_{1}, \lambda_{2}) =
\begin{cases}
\frac {(y+\lambda_{2})^{\lambda_1} - 1} {\lambda_{1}} & \mbox{when } \l |
578 | How should I transform non-negative data including zeros? | I'm presuming that zero != missing data, as that's an entirely different question.
When thinking about how to handle zeros in multiple linear regression, I tend to consider how many zeros do we actually have?
Only a couple of zeros
If I have a single zero in a reasonably large data set, I tend to:
Remove the point, ta... | How should I transform non-negative data including zeros? | I'm presuming that zero != missing data, as that's an entirely different question.
When thinking about how to handle zeros in multiple linear regression, I tend to consider how many zeros do we actual | How should I transform non-negative data including zeros?
I'm presuming that zero != missing data, as that's an entirely different question.
When thinking about how to handle zeros in multiple linear regression, I tend to consider how many zeros do we actually have?
Only a couple of zeros
If I have a single zero in a r... | How should I transform non-negative data including zeros?
I'm presuming that zero != missing data, as that's an entirely different question.
When thinking about how to handle zeros in multiple linear regression, I tend to consider how many zeros do we actual |
579 | How should I transform non-negative data including zeros? | If you want something quick and dirty why not use the square root? | How should I transform non-negative data including zeros? | If you want something quick and dirty why not use the square root? | How should I transform non-negative data including zeros?
If you want something quick and dirty why not use the square root? | How should I transform non-negative data including zeros?
If you want something quick and dirty why not use the square root? |
580 | How should I transform non-negative data including zeros? | Comparing the answer provided in by @RobHyndman to a log-plus-one transformation extended to negative values with the form:
$$T(x) = \text{sign}(x) \cdot \log{\left(|x|+1\right)} $$
(As Nick Cox pointed out in the comments, this is known as the 'neglog' transformation)
r = -1000:1000
l = sign(r)*log1p(abs(r))
l = l/ma... | How should I transform non-negative data including zeros? | Comparing the answer provided in by @RobHyndman to a log-plus-one transformation extended to negative values with the form:
$$T(x) = \text{sign}(x) \cdot \log{\left(|x|+1\right)} $$
(As Nick Cox point | How should I transform non-negative data including zeros?
Comparing the answer provided in by @RobHyndman to a log-plus-one transformation extended to negative values with the form:
$$T(x) = \text{sign}(x) \cdot \log{\left(|x|+1\right)} $$
(As Nick Cox pointed out in the comments, this is known as the 'neglog' transfor... | How should I transform non-negative data including zeros?
Comparing the answer provided in by @RobHyndman to a log-plus-one transformation extended to negative values with the form:
$$T(x) = \text{sign}(x) \cdot \log{\left(|x|+1\right)} $$
(As Nick Cox point |
581 | How should I transform non-negative data including zeros? | I assume you have continuous data.
If the data include zeros this means you have a spike on zero which may be due to some particular aspect of your data. It appears for example in wind energy, wind below 2 m/s produce zero power (it is called cut in) and wind over (something around) 25 m/s also produce zero power (for... | How should I transform non-negative data including zeros? | I assume you have continuous data.
If the data include zeros this means you have a spike on zero which may be due to some particular aspect of your data. It appears for example in wind energy, wind b | How should I transform non-negative data including zeros?
I assume you have continuous data.
If the data include zeros this means you have a spike on zero which may be due to some particular aspect of your data. It appears for example in wind energy, wind below 2 m/s produce zero power (it is called cut in) and wind o... | How should I transform non-negative data including zeros?
I assume you have continuous data.
If the data include zeros this means you have a spike on zero which may be due to some particular aspect of your data. It appears for example in wind energy, wind b |
582 | How should I transform non-negative data including zeros? | Since the two-parameter fit Box-Cox has been proposed, here's some R to fit input data, run an arbitrary function on it (e.g. time series forecasting), and then return the inverted output:
# Two-parameter Box-Cox function
boxcox.f <- function(x, lambda1, lambda2) {
if (lambda1!=0) {
return(((x + lambda2) ^ lambda... | How should I transform non-negative data including zeros? | Since the two-parameter fit Box-Cox has been proposed, here's some R to fit input data, run an arbitrary function on it (e.g. time series forecasting), and then return the inverted output:
# Two-param | How should I transform non-negative data including zeros?
Since the two-parameter fit Box-Cox has been proposed, here's some R to fit input data, run an arbitrary function on it (e.g. time series forecasting), and then return the inverted output:
# Two-parameter Box-Cox function
boxcox.f <- function(x, lambda1, lambda2... | How should I transform non-negative data including zeros?
Since the two-parameter fit Box-Cox has been proposed, here's some R to fit input data, run an arbitrary function on it (e.g. time series forecasting), and then return the inverted output:
# Two-param |
583 | How should I transform non-negative data including zeros? | The Yeo-Johnson power transformation discussed here has excellent properties designed to handle zeros and negatives while building on the strengths of Box Cox power transformation. This is what I typically go to when I am dealing with zeros or negative data.
Here is a summary of transformations with pros/cons to illus... | How should I transform non-negative data including zeros? | The Yeo-Johnson power transformation discussed here has excellent properties designed to handle zeros and negatives while building on the strengths of Box Cox power transformation. This is what I typi | How should I transform non-negative data including zeros?
The Yeo-Johnson power transformation discussed here has excellent properties designed to handle zeros and negatives while building on the strengths of Box Cox power transformation. This is what I typically go to when I am dealing with zeros or negative data.
He... | How should I transform non-negative data including zeros?
The Yeo-Johnson power transformation discussed here has excellent properties designed to handle zeros and negatives while building on the strengths of Box Cox power transformation. This is what I typi |
584 | How should I transform non-negative data including zeros? | Suppose Y is the amount of money each American spends on a new car in a given year (total purchase price). Y will spike at 0; will have no values at all between 0 and about 12,000; and will take other values mostly in the teens, twenties and thirties of thousands. Predictors would be proxies for the level of need and... | How should I transform non-negative data including zeros? | Suppose Y is the amount of money each American spends on a new car in a given year (total purchase price). Y will spike at 0; will have no values at all between 0 and about 12,000; and will take othe | How should I transform non-negative data including zeros?
Suppose Y is the amount of money each American spends on a new car in a given year (total purchase price). Y will spike at 0; will have no values at all between 0 and about 12,000; and will take other values mostly in the teens, twenties and thirties of thousan... | How should I transform non-negative data including zeros?
Suppose Y is the amount of money each American spends on a new car in a given year (total purchase price). Y will spike at 0; will have no values at all between 0 and about 12,000; and will take othe |
585 | How should I transform non-negative data including zeros? | To clarify how to deal with the log of zero in regression models, we have written a pedagogical paper explaining the best solution and the common mistakes people make in practice. We also came out with a new solution to tackle this issue.
You can find the paper by clicking here: https://ssrn.com/abstract=3444996
First... | How should I transform non-negative data including zeros? | To clarify how to deal with the log of zero in regression models, we have written a pedagogical paper explaining the best solution and the common mistakes people make in practice. We also came out wit | How should I transform non-negative data including zeros?
To clarify how to deal with the log of zero in regression models, we have written a pedagogical paper explaining the best solution and the common mistakes people make in practice. We also came out with a new solution to tackle this issue.
You can find the paper ... | How should I transform non-negative data including zeros?
To clarify how to deal with the log of zero in regression models, we have written a pedagogical paper explaining the best solution and the common mistakes people make in practice. We also came out wit |
586 | How should I transform non-negative data including zeros? | I had the same problem with data and no transformation would give reasonable distribution. I came up with the following idea. I would appreciate if someone decide whether it is worth utilising as I am not a statistitian.
We may adopt the assumption that 0 is not equal to 0. There is a hidden continuous value which we ... | How should I transform non-negative data including zeros? | I had the same problem with data and no transformation would give reasonable distribution. I came up with the following idea. I would appreciate if someone decide whether it is worth utilising as I a | How should I transform non-negative data including zeros?
I had the same problem with data and no transformation would give reasonable distribution. I came up with the following idea. I would appreciate if someone decide whether it is worth utilising as I am not a statistitian.
We may adopt the assumption that 0 is no... | How should I transform non-negative data including zeros?
I had the same problem with data and no transformation would give reasonable distribution. I came up with the following idea. I would appreciate if someone decide whether it is worth utilising as I a |
587 | How should I transform non-negative data including zeros? | Depending on the problem's context, it may be useful to apply quantile transformations.
The idea itself is simple*, given a sample $x_1, \dots, x_n$, compute for each $i \in \{1, \dots, n\}$ the respective empirical cumulative density function values $F(x_i) = c_i$, then map $c_i$ to another distribution via the quanti... | How should I transform non-negative data including zeros? | Depending on the problem's context, it may be useful to apply quantile transformations.
The idea itself is simple*, given a sample $x_1, \dots, x_n$, compute for each $i \in \{1, \dots, n\}$ the respe | How should I transform non-negative data including zeros?
Depending on the problem's context, it may be useful to apply quantile transformations.
The idea itself is simple*, given a sample $x_1, \dots, x_n$, compute for each $i \in \{1, \dots, n\}$ the respective empirical cumulative density function values $F(x_i) = c... | How should I transform non-negative data including zeros?
Depending on the problem's context, it may be useful to apply quantile transformations.
The idea itself is simple*, given a sample $x_1, \dots, x_n$, compute for each $i \in \{1, \dots, n\}$ the respe |
588 | Intuitive explanation for dividing by $n-1$ when calculating standard deviation? | The standard deviation calculated with a divisor of $n-1$ is a standard deviation calculated from the sample as an estimate of the standard deviation of the population from which the sample was drawn. Because the observed values fall, on average, closer to the sample mean than to the population mean, the standard devia... | Intuitive explanation for dividing by $n-1$ when calculating standard deviation? | The standard deviation calculated with a divisor of $n-1$ is a standard deviation calculated from the sample as an estimate of the standard deviation of the population from which the sample was drawn. | Intuitive explanation for dividing by $n-1$ when calculating standard deviation?
The standard deviation calculated with a divisor of $n-1$ is a standard deviation calculated from the sample as an estimate of the standard deviation of the population from which the sample was drawn. Because the observed values fall, on a... | Intuitive explanation for dividing by $n-1$ when calculating standard deviation?
The standard deviation calculated with a divisor of $n-1$ is a standard deviation calculated from the sample as an estimate of the standard deviation of the population from which the sample was drawn. |
589 | Intuitive explanation for dividing by $n-1$ when calculating standard deviation? | By definition, variance is calculated by taking the sum of squared differences from the mean and dividing by the size. We have the general formula
$\sigma^2= \frac{\sum_{i}^{N}(X_i-\mu)^2}{N}$ where $\mu$ is the mean and $N$ is the size of the population.
According to this definition, variance of the a sample (e.g. s... | Intuitive explanation for dividing by $n-1$ when calculating standard deviation? | By definition, variance is calculated by taking the sum of squared differences from the mean and dividing by the size. We have the general formula
$\sigma^2= \frac{\sum_{i}^{N}(X_i-\mu)^2}{N}$ where | Intuitive explanation for dividing by $n-1$ when calculating standard deviation?
By definition, variance is calculated by taking the sum of squared differences from the mean and dividing by the size. We have the general formula
$\sigma^2= \frac{\sum_{i}^{N}(X_i-\mu)^2}{N}$ where $\mu$ is the mean and $N$ is the size o... | Intuitive explanation for dividing by $n-1$ when calculating standard deviation?
By definition, variance is calculated by taking the sum of squared differences from the mean and dividing by the size. We have the general formula
$\sigma^2= \frac{\sum_{i}^{N}(X_i-\mu)^2}{N}$ where |
590 | Intuitive explanation for dividing by $n-1$ when calculating standard deviation? | A common one is that the definition of variance (of a distribution) is the second moment recentered around a known, definite mean, whereas the estimator uses an estimated mean. This loss of a degree of freedom (given the mean, you can reconstitute the dataset with knowledge of just $n-1$ of the data values) requires t... | Intuitive explanation for dividing by $n-1$ when calculating standard deviation? | A common one is that the definition of variance (of a distribution) is the second moment recentered around a known, definite mean, whereas the estimator uses an estimated mean. This loss of a degree | Intuitive explanation for dividing by $n-1$ when calculating standard deviation?
A common one is that the definition of variance (of a distribution) is the second moment recentered around a known, definite mean, whereas the estimator uses an estimated mean. This loss of a degree of freedom (given the mean, you can rec... | Intuitive explanation for dividing by $n-1$ when calculating standard deviation?
A common one is that the definition of variance (of a distribution) is the second moment recentered around a known, definite mean, whereas the estimator uses an estimated mean. This loss of a degree |
591 | Intuitive explanation for dividing by $n-1$ when calculating standard deviation? | This is a total intuition, but the simplest answer is that is a correction made to make standard deviation of one-element sample undefined rather than 0. | Intuitive explanation for dividing by $n-1$ when calculating standard deviation? | This is a total intuition, but the simplest answer is that is a correction made to make standard deviation of one-element sample undefined rather than 0. | Intuitive explanation for dividing by $n-1$ when calculating standard deviation?
This is a total intuition, but the simplest answer is that is a correction made to make standard deviation of one-element sample undefined rather than 0. | Intuitive explanation for dividing by $n-1$ when calculating standard deviation?
This is a total intuition, but the simplest answer is that is a correction made to make standard deviation of one-element sample undefined rather than 0. |
592 | Intuitive explanation for dividing by $n-1$ when calculating standard deviation? | It is well-known (or easily proved) that the quadratic $\alpha z^2 + 2\beta z + \gamma$ has an extremum at $z = -\frac{\beta}{\alpha}$ which point is midway between the roots $\frac{-\beta - \sqrt{\beta^2-\alpha\gamma}}{\alpha}$ and $\frac{-\beta + \sqrt{\beta^2-\alpha\gamma}}{\alpha}$ of the quadratic.
This shows that... | Intuitive explanation for dividing by $n-1$ when calculating standard deviation? | It is well-known (or easily proved) that the quadratic $\alpha z^2 + 2\beta z + \gamma$ has an extremum at $z = -\frac{\beta}{\alpha}$ which point is midway between the roots $\frac{-\beta - \sqrt{\be | Intuitive explanation for dividing by $n-1$ when calculating standard deviation?
It is well-known (or easily proved) that the quadratic $\alpha z^2 + 2\beta z + \gamma$ has an extremum at $z = -\frac{\beta}{\alpha}$ which point is midway between the roots $\frac{-\beta - \sqrt{\beta^2-\alpha\gamma}}{\alpha}$ and $\frac... | Intuitive explanation for dividing by $n-1$ when calculating standard deviation?
It is well-known (or easily proved) that the quadratic $\alpha z^2 + 2\beta z + \gamma$ has an extremum at $z = -\frac{\beta}{\alpha}$ which point is midway between the roots $\frac{-\beta - \sqrt{\be |
593 | Intuitive explanation for dividing by $n-1$ when calculating standard deviation? | Why divide by $n-1$ rather than $n$? Because it is customary, and results in an unbiased estimate of the variance. However, it results in a biased (low) estimate of the standard deviation, as can be seen by applying Jensen's inequality to the concave function, square root.
So what's so great about having an unbiased es... | Intuitive explanation for dividing by $n-1$ when calculating standard deviation? | Why divide by $n-1$ rather than $n$? Because it is customary, and results in an unbiased estimate of the variance. However, it results in a biased (low) estimate of the standard deviation, as can be s | Intuitive explanation for dividing by $n-1$ when calculating standard deviation?
Why divide by $n-1$ rather than $n$? Because it is customary, and results in an unbiased estimate of the variance. However, it results in a biased (low) estimate of the standard deviation, as can be seen by applying Jensen's inequality to ... | Intuitive explanation for dividing by $n-1$ when calculating standard deviation?
Why divide by $n-1$ rather than $n$? Because it is customary, and results in an unbiased estimate of the variance. However, it results in a biased (low) estimate of the standard deviation, as can be s |
594 | Intuitive explanation for dividing by $n-1$ when calculating standard deviation? | You can gain a deeper understanding of the $n-1$ term through geometry alone, not just why it's not $n$ but why it takes exactly this form, but you may first need to build up your intuition cope with $n$-dimensional geometry. From there, however, it's a small step to a deeper understanding of degrees of freedom in line... | Intuitive explanation for dividing by $n-1$ when calculating standard deviation? | You can gain a deeper understanding of the $n-1$ term through geometry alone, not just why it's not $n$ but why it takes exactly this form, but you may first need to build up your intuition cope with | Intuitive explanation for dividing by $n-1$ when calculating standard deviation?
You can gain a deeper understanding of the $n-1$ term through geometry alone, not just why it's not $n$ but why it takes exactly this form, but you may first need to build up your intuition cope with $n$-dimensional geometry. From there, h... | Intuitive explanation for dividing by $n-1$ when calculating standard deviation?
You can gain a deeper understanding of the $n-1$ term through geometry alone, not just why it's not $n$ but why it takes exactly this form, but you may first need to build up your intuition cope with |
595 | Intuitive explanation for dividing by $n-1$ when calculating standard deviation? | The estimator of the population variance is biased when applied on a sample of the population. In order to adjust for that bias on needs to divide by n-1 instead of n. One can show mathematically that the estimator of the sample variance is unbiased when we divide by n-1 instead of n. A formal proof is provided here:
h... | Intuitive explanation for dividing by $n-1$ when calculating standard deviation? | The estimator of the population variance is biased when applied on a sample of the population. In order to adjust for that bias on needs to divide by n-1 instead of n. One can show mathematically that | Intuitive explanation for dividing by $n-1$ when calculating standard deviation?
The estimator of the population variance is biased when applied on a sample of the population. In order to adjust for that bias on needs to divide by n-1 instead of n. One can show mathematically that the estimator of the sample variance i... | Intuitive explanation for dividing by $n-1$ when calculating standard deviation?
The estimator of the population variance is biased when applied on a sample of the population. In order to adjust for that bias on needs to divide by n-1 instead of n. One can show mathematically that |
596 | Intuitive explanation for dividing by $n-1$ when calculating standard deviation? | At the suggestion of whuber, this answer has been copied over from another similar question.
Bessel's correction is adopted to correct for bias in using the sample variance as an estimator of the true variance. The bias in the uncorrected statistic occurs because the sample mean is closer to the middle of the observat... | Intuitive explanation for dividing by $n-1$ when calculating standard deviation? | At the suggestion of whuber, this answer has been copied over from another similar question.
Bessel's correction is adopted to correct for bias in using the sample variance as an estimator of the true | Intuitive explanation for dividing by $n-1$ when calculating standard deviation?
At the suggestion of whuber, this answer has been copied over from another similar question.
Bessel's correction is adopted to correct for bias in using the sample variance as an estimator of the true variance. The bias in the uncorrected... | Intuitive explanation for dividing by $n-1$ when calculating standard deviation?
At the suggestion of whuber, this answer has been copied over from another similar question.
Bessel's correction is adopted to correct for bias in using the sample variance as an estimator of the true |
597 | Intuitive explanation for dividing by $n-1$ when calculating standard deviation? | Sample variance can be thought of to be the exact mean of the pairwise "energy" $(x_i-x_j)^2/2$ between all sample points. The definition of sample variance then becomes
$$ s^2 = \frac{2}{n(n-1)}\sum_{i< j}\frac{(x_i-x_j)^2}{2} = \frac{1}{n-1}\sum_{i=1}^n(x_i-\bar{x})^2 .$$
This also agrees with defining variance of a ... | Intuitive explanation for dividing by $n-1$ when calculating standard deviation? | Sample variance can be thought of to be the exact mean of the pairwise "energy" $(x_i-x_j)^2/2$ between all sample points. The definition of sample variance then becomes
$$ s^2 = \frac{2}{n(n-1)}\sum_ | Intuitive explanation for dividing by $n-1$ when calculating standard deviation?
Sample variance can be thought of to be the exact mean of the pairwise "energy" $(x_i-x_j)^2/2$ between all sample points. The definition of sample variance then becomes
$$ s^2 = \frac{2}{n(n-1)}\sum_{i< j}\frac{(x_i-x_j)^2}{2} = \frac{1}{... | Intuitive explanation for dividing by $n-1$ when calculating standard deviation?
Sample variance can be thought of to be the exact mean of the pairwise "energy" $(x_i-x_j)^2/2$ between all sample points. The definition of sample variance then becomes
$$ s^2 = \frac{2}{n(n-1)}\sum_ |
598 | Intuitive explanation for dividing by $n-1$ when calculating standard deviation? | The sample mean is defined as $\bar{X} = \frac{1}{n}\sum_{i=1}^{n} X_i$, which is quite intuitive. But the sample variance is $S^2 = \frac{1}{n-1}\sum_{i=1}^{n} (X_i - \bar{X})^2$. Where did the $n - 1$ come from ?
To answer this question, we must go back to the definition of an unbiased estimator. An unbiased estim... | Intuitive explanation for dividing by $n-1$ when calculating standard deviation? | The sample mean is defined as $\bar{X} = \frac{1}{n}\sum_{i=1}^{n} X_i$, which is quite intuitive. But the sample variance is $S^2 = \frac{1}{n-1}\sum_{i=1}^{n} (X_i - \bar{X})^2$. Where did the $n - | Intuitive explanation for dividing by $n-1$ when calculating standard deviation?
The sample mean is defined as $\bar{X} = \frac{1}{n}\sum_{i=1}^{n} X_i$, which is quite intuitive. But the sample variance is $S^2 = \frac{1}{n-1}\sum_{i=1}^{n} (X_i - \bar{X})^2$. Where did the $n - 1$ come from ?
To answer this question... | Intuitive explanation for dividing by $n-1$ when calculating standard deviation?
The sample mean is defined as $\bar{X} = \frac{1}{n}\sum_{i=1}^{n} X_i$, which is quite intuitive. But the sample variance is $S^2 = \frac{1}{n-1}\sum_{i=1}^{n} (X_i - \bar{X})^2$. Where did the $n - |
599 | Intuitive explanation for dividing by $n-1$ when calculating standard deviation? | Suppose that you have a random phenomenon. Suppose again that you only get one $N=1$ sample, or realization, $x$. Without further assumptions, your "only" reasonable choice for a sample average is $\overline{m}=x$. If you do not subtract $1$ from your denominator, the (uncorrect) sample variance would be $$ V=\frac{... | Intuitive explanation for dividing by $n-1$ when calculating standard deviation? | Suppose that you have a random phenomenon. Suppose again that you only get one $N=1$ sample, or realization, $x$. Without further assumptions, your "only" reasonable choice for a sample average is $ | Intuitive explanation for dividing by $n-1$ when calculating standard deviation?
Suppose that you have a random phenomenon. Suppose again that you only get one $N=1$ sample, or realization, $x$. Without further assumptions, your "only" reasonable choice for a sample average is $\overline{m}=x$. If you do not subtract... | Intuitive explanation for dividing by $n-1$ when calculating standard deviation?
Suppose that you have a random phenomenon. Suppose again that you only get one $N=1$ sample, or realization, $x$. Without further assumptions, your "only" reasonable choice for a sample average is $ |
600 | Intuitive explanation for dividing by $n-1$ when calculating standard deviation? | The intuitive reason for the $n-1$ is that the $n$ deviations in the calculation of the standard deviation are not independent. There is one constraint which is that the sum of the deviations is zero. When we take that into account we are effectively dealing with $n-1$ quantities rather than $n$. (Geometrically the ... | Intuitive explanation for dividing by $n-1$ when calculating standard deviation? | The intuitive reason for the $n-1$ is that the $n$ deviations in the calculation of the standard deviation are not independent. There is one constraint which is that the sum of the deviations is zero | Intuitive explanation for dividing by $n-1$ when calculating standard deviation?
The intuitive reason for the $n-1$ is that the $n$ deviations in the calculation of the standard deviation are not independent. There is one constraint which is that the sum of the deviations is zero. When we take that into account we ar... | Intuitive explanation for dividing by $n-1$ when calculating standard deviation?
The intuitive reason for the $n-1$ is that the $n$ deviations in the calculation of the standard deviation are not independent. There is one constraint which is that the sum of the deviations is zero |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.