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Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work

Takeaways:

Q: What is the "Bayesian Workflow" book about, and who is it for?
A: It covers what the three authors know that isn't already in Bayesian Data Analysis (BDA3) or Statistical Rethinking, organized around case studies that walk through full analyses end to end rather than just giving a recommendation. It's not an introduction to Bayesian inference -- it assumes you already know the basics -- but a guide to making theoretically informed, professional decisions at the many branching points a real analysis involves that source books rarely acknowledge.

Q: What's a concrete way to report Bayesian results without just handing over a posterior distribution?
A: Report a few named scenarios from the distribution, such as pessimistic, median, and optimistic. This is easier to discuss than a full posterior and helps shift the conversation toward what would move outcomes from the median toward the optimistic case.

Chapters:
00:00:00 Who are this episode's three guests, and what is Bayesian Workflow?
00:02:35 What's new: the LBS Instagram account and the Carnegie Mellon workshop?
00:05:22 What is Richard McElreath's origin story, from anthropology to statistics?
00:18:22 What is the elevator pitch for the Bayesian Workflow book?
00:20:12 Where does workflow sit between statistical theory and case studies?
00:27:21 Why express your scientific background in a generative model?
00:35:04 What came out of the LBS listener contest?
00:36:43 How is a Bayesian workflow different from a pipeline?
00:39:03 What is reverse Bayes, and how does it help with prior sensitivity?
00:43:53 How do Bayesians reinterpret non-Bayesian methods?
00:45:02 How is the Bayesian Workflow book structured?
00:46:51 Who is Dorota, the LBS contest grand prize winner?
00:52:24 When does a hierarchical model stop being an innocuous assumption?
00:58:17 Can multilevel regression and poststratification pool detection across sites?
00:59:32 Why start with a big generative simulation before the statistical model?
01:02:05 What is the "secret weapon" of comparing shrinkage to fixed-effects estimates?
01:11:02 How do you detect which assumptions are actually driving your inference?
01:15:24 How do you get regulated industries to accept a posterior instead of a score?
01:22:04 Should statisticians soften uncertainty for decision makers?
01:23:11 Why report three scenarios instead of a single number?
01:25:16 How do you communicate survival probabilities to cancer doctors?
01:27:51 How do you handle a leaky instrument in causal inference?
01:29:16 What is a principal stratification model?
01:34:47 What are the three authors working on next?
01:39:53 If you had unlimited time and resources, which problem would you solve?
01:41:26 Could statistical workflow be made more axiomatic?
01:42:03 Which great scientific mind would you have dinner with?

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