
Bayesian Data Analysis, Third Edition [pdf] - malshe
https://users.aalto.fi/~ave/BDA3.pdf
======
mwexler
Another good one to read is Statistical Rethinking via
[https://xcelab.net/rm/statistical-
rethinking/](https://xcelab.net/rm/statistical-rethinking/). A bit easier to
understand than Gelman's book, but together, these give you an amazing
foundation in modern bayesian analysis.

Cam's book, mentioned also in the comments, is also wonderful.

~~~
oarabbus_
At the risk of sounding quite silly, how do people read these textbooks? Do
people (who are not in graduate studies) actually work through entire books,
or just particular chapters?

I grinded through textbooks during my graduate studies, but I had to, in order
to complete the HW and pass the courses.

But since joining industry I've not been able to actually work through a
textbook - when I try to attempt the problems, I'll find a couple have passed
and only one or two problems have been completed - I simply find it a
challenge to find the time to work through book exercises.

~~~
grayclhn
For something in my field, I "speed read" it. (In quotes because I have no
idea if this is how actual speed reading works.) I.e. set aside one or two
blocks of 4 hours or so and commit to finishing the book in that window.

I usually don't retain a ton, but the big benefit is that I know where to find
the relevant sections when I need them in the future, and have some sort of
big-picture view of how they fit together.

~~~
kd5bjo
The book "How to Read a Book" comes up here occasionally, and this is (I
think) the second level of their 4-level system, after examining the table of
contents and end matter.

~~~
grayclhn
I may have gotten it from there, tbh. That first step looks familiar.

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metakermit
If someone wants a more interactive companion-book targeted more towards
Python developers, check out "Probabilistic Programming & Bayesian Methods for
Hackers":

[http://camdavidsonpilon.github.io/Probabilistic-
Programming-...](http://camdavidsonpilon.github.io/Probabilistic-Programming-
and-Bayesian-Methods-for-Hackers/)

Relevant quote:

> "I ... read this book ... I like it!" \- Andrew Gelman

~~~
xitrium
This is weirdly deliberately misquoted, maybe as a joke? full:
[https://statmodeling.stat.columbia.edu/2013/07/21/bayes-
rela...](https://statmodeling.stat.columbia.edu/2013/07/21/bayes-related/)

~~~
rahimnathwani
It says above that quote: "These are satirical, but real"

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petulla
Gelman et al also updated Regression and Other Stories's example page
[https://avehtari.github.io/ROS-Examples/](https://avehtari.github.io/ROS-
Examples/)

------
rintakumpu
Lecture videos to go with it
[https://aalto.cloud.panopto.eu/Panopto/Pages/Sessions/List.a...](https://aalto.cloud.panopto.eu/Panopto/Pages/Sessions/List.aspx#folderID=%22f0ec3a25-9e23-4935-873b-a9f401646812%22).

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noctilux
This is a _fantastic_ book. If anyone's worried it's too technical, I'd say
that it's not as dry as it might look at first glance. There's lots of
practical advice and there's actually not that much heavy maths.

~~~
anthony_doan
> there's actually not that much heavy maths.

Uh... sure if you know measure theory.

The later chapter especially in Dirichlet chapters assume you know measure
theory.

~~~
stenecdote
I don't think this is true having read some of the later chapters and not
knowing measure theory. And if you don't trust me, Gelman doesn't know measure
theory either
([https://statmodeling.stat.columbia.edu/2008/01/14/what_to_le...](https://statmodeling.stat.columbia.edu/2008/01/14/what_to_learn_i/))
and he wrote the book...

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lorenzfx
Has somebody read the book and can let us know how it compares to similar
books? Would you recommend it as an introduction to topic?

I've always heard that it's a bit on the dry side of things, but haven't
actually read it myself.

~~~
martingoodson
It's a great book if you want to understand bayesian modeling in detail. Its
not 'dry' as in boring - it's an interesting read.

If you want something less technical then read Gelman and Hill 'Data Analysis
Using Regression and Multilevel/Hierarchical Models', which is also great.
More for scientists than statisticians, I'd say.

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clircle
Great book loaded with practical information. I'll also recommend Christian
Robert's The Bayesian Choice for the more math-y decision theory crowd.

~~~
kgwgk
From the same author but more practical:
[https://link.springer.com/book/10.1007/978-1-4614-8687-9](https://link.springer.com/book/10.1007/978-1-4614-8687-9)

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anthony_doan
This is the bible for Bayesian statistic. It's phd imo or a senior level grad
student.

I was hoping he updated the other book for Hierarchical modeling with rStan.

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whatok
The book is also available on Prof Gelman's department site which I would
probably link to instead:

[http://www.stat.columbia.edu/~gelman/book/](http://www.stat.columbia.edu/~gelman/book/)

~~~
ajaalto
The first link points to Aki Vehtari's personal page. He is one of the co-
authors.

~~~
malshe
Thanks for clarifying this! I did not realize the domain name will cause
skepticism :)

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cambalache
For the unaware I also recommend prof Gelman blog.
[https://statmodeling.stat.columbia.edu/](https://statmodeling.stat.columbia.edu/)

