Bayesian Interest Group

Hi everyone!

Would love to have a space where we can share resources we've found illuminating and helpful. What books, articles, videos, podcasts, tools have shaped your Bayesian thinking?

Enrico

I'll go first! 

I've found Bernoulli's Fallacy by Aubrey Clayton very approachable and helpful. I've been recommending to everyone lately :)

I've also been working on a couple of Bayesian Networks at work. And I've found Risk Assessment with Decision Analysis with Bayesian Networks (2e) by Norman Fenton and Martin Neil. I've found it easy to read and bridge the DA language and tools I'm familiar and the Bayes nets I've been learning about.

Hi everyone!

Would love to have a space where we can share resources we've found illuminating and helpful. What books, articles, videos, podcasts, tools have shaped your Bayesian thinking?

Enrico

I've got 7 works I'd put forth (one you've already mentioned), I'd classify them as "must reads", "highly recommended" and "for the ambitious".  Here they are listed in the order (by class) I'd work through them if I had to start from scratch.

Must reads (and each isn't very long but they are the intuition builders):

1. The book that started it all for me was "The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, & Emerged Triumphant from Two Centuries of Controversy" by Sharon Bertsch McGrayne, it will inspire you. 
2. Will Kurt's book "Bayesian Statistics the Fun Way: Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks" is a fast read and gets you using Bayesian AB Testing in a few days. It assumes no background in Bayesian Stats, if you know the basics I'd skip this and move on to Winkler's book below.
3. "Bayesian Inference and Decision: 2nd Edition" by Robert L. Winkler is a book that unifies Bayesian decision theory and Decision Analysis
4. Clayton's book "Bernoulli's Fallacy" is a must-read I think, its kinda like the other side of the story McGrayne tells.  Must read.

Highly recommended (these are longer but they are practitioner tool builders):

1. In terms of mechanics, Richard McElreath's "Statistical Rethinking: A Bayesian Course with Examples in R and STAN (Chapman & Hall/CRC Texts in Statistical Science)" is a pedagogical masterpiece. 
   • It's a course he teaches, so this isn't a quick read either, however it's the best resource out there to go from zero to modeler in modern Bayesian inference. 
   • YouTube video series is excellent: https://youtube.com/playlist?list=PLDcUM9US4XdNOlqSyhe38US8mFgmqzI14&si=a_xxZW3CeJHOjy32
   • However, this does require you to use "R" (there is a Python version of the code on his website).  But we're out of the Excel world now.
2. "Understanding Uncertainty" by Dennis V Lindley was huge in me understanding Bayes conceptually before getting into all the math. Not a fast read but lays a rigorous foundation.
3. Along the lines of more technical work I'd be remiss if I didn't mention Andrew Gelman's "Bayesian Data Analysis: 3rd Edition"
   • For those who want to use Bayesian Inference seriously, paired with McElreath you've got an excellent grad-student level survey of the field.
   • Not a quick read, needs R or Python
   • But free and open-sourced content: https://sites.stat.columbia.edu/gelman/book/

For the ambitious (for those who want to join the debate):

1. "Introduction to Statistical Decision Theory" Howard Raiffa, John Pratt and Robert Schlaifer.  The OG text on the topic from the pioneers of conjugate priors, decision-analysis and most of the stuff we do as practitioners.
2. "Probability Theory: The Logic of Science" by Edwin T Jaynes.  This is a hard book... won't sugar coat it.  But the structural dividends are huge once you understand what he's saying
3. "Bayesian Theory" by Jose Bernardo and Adrian Smith.  Bernardo is the godfather of non-parametric Bayes, or creating a model where you don't even know what functions to use to represent your phenomena of interest.

If I had to pick, that'd be my list.  #bayes_readinglist #resources

Here's to making coherent inferences!  

------------------------------
Sheldon Bernard
Founder
A440 Management LLC
Cary, NC, USA
------------------------------
-------------------------------------------
Original Message:
Sent: 04-21-2026 09:01
From: Enrico Manlapig
Subject: What resources do you find helpful?

Hi everyone!

Would love to have a space where we can share resources we've found illuminating and helpful. What books, articles, videos, podcasts, tools have shaped your Bayesian thinking?

Enrico

------------------------------
Enrico Manlapig
Principal
Chick-fil-A
Atlanta GA
------------------------------

I like both your lists. Bernoulli's Fallacy by Aubrey Clayton is one of my favorites. I've read/skimmed most of the must reads on your list Sheldon. I got Jaynes book but no sure I'm getting much out of it.

A few of my favorite Bayesian books in disguise are Think Again, by Adam Grant; Quit: The Power of Knowing When to Walk Away, by Annie Duke, and The Signal and the Noise (which I haven't picked up in awhile), none of which discuss Bayes directly but are all about updating your beliefs given new information.

------------------------------
Steven Glickman
Sr. Systems Engineer
Boeing
Kirkland WA

Original Message:
Sent: 04-22-2026 15:11
From: Sheldon Bernard
Subject: What resources do you find helpful?

I've got 7 works I'd put forth (one you've already mentioned), I'd classify them as "must reads", "highly recommended" and "for the ambitious".  Here they are listed in the order (by class) I'd work through them if I had to start from scratch.

Must reads (and each isn't very long but they are the intuition builders):

1. The book that started it all for me was "The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, & Emerged Triumphant from Two Centuries of Controversy" by Sharon Bertsch McGrayne, it will inspire you. 
2. Will Kurt's book "Bayesian Statistics the Fun Way: Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks" is a fast read and gets you using Bayesian AB Testing in a few days. It assumes no background in Bayesian Stats, if you know the basics I'd skip this and move on to Winkler's book below.
3. "Bayesian Inference and Decision: 2nd Edition" by Robert L. Winkler is a book that unifies Bayesian decision theory and Decision Analysis
4. Clayton's book "Bernoulli's Fallacy" is a must-read I think, its kinda like the other side of the story McGrayne tells.  Must read.

Highly recommended (these are longer but they are practitioner tool builders):

1. In terms of mechanics, Richard McElreath's "Statistical Rethinking: A Bayesian Course with Examples in R and STAN (Chapman & Hall/CRC Texts in Statistical Science)" is a pedagogical masterpiece. 
   • It's a course he teaches, so this isn't a quick read either, however it's the best resource out there to go from zero to modeler in modern Bayesian inference. 
   • YouTube video series is excellent: https://youtube.com/playlist?list=PLDcUM9US4XdNOlqSyhe38US8mFgmqzI14&si=a_xxZW3CeJHOjy32
   • However, this does require you to use "R" (there is a Python version of the code on his website).  But we're out of the Excel world now.
2. "Understanding Uncertainty" by Dennis V Lindley was huge in me understanding Bayes conceptually before getting into all the math. Not a fast read but lays a rigorous foundation.
3. Along the lines of more technical work I'd be remiss if I didn't mention Andrew Gelman's "Bayesian Data Analysis: 3rd Edition"
   • For those who want to use Bayesian Inference seriously, paired with McElreath you've got an excellent grad-student level survey of the field.
   • Not a quick read, needs R or Python
   • But free and open-sourced content: https://sites.stat.columbia.edu/gelman/book/

For the ambitious (for those who want to join the debate):

1. "Introduction to Statistical Decision Theory" Howard Raiffa, John Pratt and Robert Schlaifer.  The OG text on the topic from the pioneers of conjugate priors, decision-analysis and most of the stuff we do as practitioners.
2. "Probability Theory: The Logic of Science" by Edwin T Jaynes.  This is a hard book... won't sugar coat it.  But the structural dividends are huge once you understand what he's saying
3. "Bayesian Theory" by Jose Bernardo and Adrian Smith.  Bernardo is the godfather of non-parametric Bayes, or creating a model where you don't even know what functions to use to represent your phenomena of interest.

If I had to pick, that'd be my list.  #bayes_readinglist #resources

Here's to making coherent inferences!  

------------------------------
Sheldon Bernard
Founder
A440 Management LLC
Cary, NC, USA
------------------------------
-------------------------------------------
Original Message:
Sent: 04-21-2026 09:01
From: Enrico Manlapig
Subject: What resources do you find helpful?

Hi everyone!

Would love to have a space where we can share resources we've found illuminating and helpful. What books, articles, videos, podcasts, tools have shaped your Bayesian thinking?

Enrico

------------------------------
Enrico Manlapig
Principal
Chick-fil-A
Atlanta GA
------------------------------