Bayesian Data Analysis, Third edition (2013) [pdf] • Hacker News Board

Re: Bayesian Data Analysis, Third edition (2013) [pdf]

#41

Post by greymalik » Mon, Sep 29, 2025, 1:29 AM UTC

kianN wrote: post #18

SilverElfin wrote: post #15

Earlier quoted context omitted.

Is there a good book that covers statistics as it is applied to testing - like for medical research or as optimization or manufacturing or whatever?

This book is very relevant to those fields. There is a common choice in statistics to either stratify or aggregate your dataset. There is an example in his book discussing efficacy trials across seven hospitals. If you stratify the data, you lose a lot of confidence, if you aggregate the data, you end up just modeling the difference between hospitals. Hierarchical modeling allows you to split your dataset under a sin…

It’s unclear which post you’re referring to - can you clarify which book you mean by “this book”?

Re: Bayesian Data Analysis, Third edition (2013) [pdf]

#42

Post by tmule » Mon, Sep 29, 2025, 2:17 AM UTC

kianN wrote: post #13

This is my favorite book on statistics. Full stop. The author Andrew Gelman created a whole new branch of Bayesian statistics with both his theoretical work on hierarchical modeling while also publishing Stan to enable practical applications of hierarchical models. It took me about a year to work through this book on the side (including the exercises) and it provided the foundation for years of fruitful research into…

“ The author Andrew Gelman created a whole new branch of Bayesian statistics ...” Love Gelman, but this is playing fast and loose with facts.

Re: Bayesian Data Analysis, Third edition (2013) [pdf]

#43

Post by itissid » Mon, Sep 29, 2025, 2:27 AM UTC

pyyxbkshed wrote: post #20

kianN wrote: post #13

This is my favorite book on statistics. Full stop. The author Andrew Gelman created a whole new branch of Bayesian statistics with both his theoretical work on hierarchical modeling while also publishing Stan to enable practical applications of hierarchical models. It took me about a year to work through this book on the side (including the exercises) and it provided the foundation for years of fruitful research into…

What is a book / course on statistics that I can go through before this so that I can understand this?

If you are near Columbia the visiting students post baccalaureate program(run by the SPS last I recall) allows you to take for credit courses in the Social Sciences department. Professor Ben Goodrich has an excellent course on Bayesian Statistics in Social Sciences which teaches it using R(now it might be in Stan).

That course is a good balance between theory and practice. It gave me a practical intuition understanding why posterior distribution of parameters and data are important and how to compute them.

I took the course in 2016 so a lot could have changed.

Re: Bayesian Data Analysis, Third edition (2013) [pdf]

#44

Post by kragen » Mon, Sep 29, 2025, 2:28 AM UTC

tmule wrote: post #42

kianN wrote: post #13

This is my favorite book on statistics. Full stop. The author Andrew Gelman created a whole new branch of Bayesian statistics with both his theoretical work on hierarchical modeling while also publishing Stan to enable practical applications of hierarchical models. It took me about a year to work through this book on the side (including the exercises) and it provided the foundation for years of fruitful research into…

“ The author Andrew Gelman created a whole new branch of Bayesian statistics ...” Love Gelman, but this is playing fast and loose with facts.

His book on hierarchical modeling with Hill has 20398 cites on Google Scholar https://scholar.google.com/scholar?cluster=94492350364273118... and Wikipedia calls him "a major contributor to statistical philosophy and methods especially in Bayesian statistics[6] and hierarchical models.[7]", which sounds like the claim is more true than false.

Re: Bayesian Data Analysis, Third edition (2013) [pdf]

#45

Post by fromMars » Mon, Sep 29, 2025, 2:40 AM UTC

j7ake wrote: post #31

Is Bayesian data analysis relevant anymore in the era of foundation models and big data?

Most definitely. Many problems do not have giant datasets and it also depends on what is your task.

Re: Bayesian Data Analysis, Third edition (2013) [pdf]

#46

Post by drnick1 » Mon, Sep 29, 2025, 2:41 AM UTC

jakubmazanec wrote: post #37

For beginners, I found Doing Bayesian Data Analysis by John Kruschke to be much better. Easier to read and comprehend.

BDA is intended for grad students I think. It's not particular "hard" as far as math goes, but it assumes a first course in mathematical statistics.

Re: Bayesian Data Analysis, Third edition (2013) [pdf]

#47

Post by nextos » Mon, Sep 29, 2025, 2:45 AM UTC

kragen wrote: post #44

tmule wrote: post #42

Earlier quoted context omitted.

“ The author Andrew Gelman created a whole new branch of Bayesian statistics ...” Love Gelman, but this is playing fast and loose with facts.

His book on hierarchical modeling with Hill has 20398 cites on Google Scholar https://scholar.google.com/scholar?cluster=94492350364273118... and Wikipedia calls him "a major contributor to statistical philosophy and methods especially in Bayesian statistics[6] and hierarchical models.[7]", which sounds like the claim is more true than false.

He co-wrote the reference textbook on the topic and made interesting methodological contributions, but Gelman acknowledges other people as creators of the theoretical underpinnings of multilevel/hierarchical modeling, including Stein or Donoho [1]. The field is quite old, one can find hierarchical models in articles that were published many decades ago.

Also, IMHO, his best work has been done describing how to do statistics. He has written somewhere I cannot find now that he sees himself as a user of mathematics, not as a creator of new theories. His book Regression and Other Stories is elementary but exceptionally well written. He describes how great Bayesian statisticians think and work, and this is invaluable.

He is updating Data Analysis Using Regression and Multilevel/Hierarchical Models to the same standard, and I guess BDA will eventually come next. As part of the refresh, I imagine everything will be ported to Stan. Interestingly, Bob Carpenter and others working on Stan are now pursuing ideas on variational inference to scale things further.

[1] https://sites.stat.columbia.edu/gelman/research/unpublished/...

Re: Bayesian Data Analysis, Third edition (2013) [pdf]

#48

Post by [deleted] » Mon, Sep 29, 2025, 2:56 AM UTC

tmule wrote: post #42

kianN wrote: post #13

This is my favorite book on statistics. Full stop. The author Andrew Gelman created a whole new branch of Bayesian statistics with both his theoretical work on hierarchical modeling while also publishing Stan to enable practical applications of hierarchical models. It took me about a year to work through this book on the side (including the exercises) and it provided the foundation for years of fruitful research into…

“ The author Andrew Gelman created a whole new branch of Bayesian statistics ...” Love Gelman, but this is playing fast and loose with facts.

[deleted]

Re: Bayesian Data Analysis, Third edition (2013) [pdf]

#49

Post by twiecki » Mon, Sep 29, 2025, 3:52 AM UTC

pyyxbkshed wrote: post #20

kianN wrote: post #13

This is my favorite book on statistics. Full stop. The author Andrew Gelman created a whole new branch of Bayesian statistics with both his theoretical work on hierarchical modeling while also publishing Stan to enable practical applications of hierarchical models. It took me about a year to work through this book on the side (including the exercises) and it provided the foundation for years of fruitful research into…

What is a book / course on statistics that I can go through before this so that I can understand this?

There is a collection of curated resources here: https://www.pymc.io/projects/docs/en/stable/learn.html

Re: Bayesian Data Analysis, Third edition (2013) [pdf]

#50

Post by kianN » Mon, Sep 29, 2025, 3:57 AM UTC

nextos wrote: post #47

kragen wrote: post #44

Earlier quoted context omitted.

His book on hierarchical modeling with Hill has 20398 cites on Google Scholar https://scholar.google.com/scholar?cluster=94492350364273118... and Wikipedia calls him "a major contributor to statistical philosophy and methods especially in Bayesian statistics[6] and hierarchical models.[7]", which sounds like the claim is more true than false.

He co-wrote the reference textbook on the topic and made interesting methodological contributions, but Gelman acknowledges other people as creators of the theoretical underpinnings of multilevel/hierarchical modeling, including Stein or Donoho [1]. The field is quite old, one can find hierarchical models in articles that were published many decades ago. Also, IMHO, his best work has been done describing how to do sta…

Totally agree and great point that hierarchical models have been around for a long time; however, these were primarily analytical, leveraging conjugate priors or requiring pretty extensive integration.

I would say his work with Stan and his writings, along with theorists like Radford Neal, really opened the door to a computational approach to hierarchical modeling. And I think this is a meaningfully different field.