Reviews of our Bayesian Workflow book from Bin Yu, David Spiegelhalter, Brad Efron, Christian Robert, Rohan Alexander, and Mine Doğucu!

Roughly speaking, Bayesian Workflow is to Bayesian Data Analysis in 2026 what Bayesian Data Analysis was to earlier Bayesian books in 1995: it builds upon everything that came before. With Bayesian Data Analysis, the big steps forward were: Going beyond Bayesian inference to also consider Bayesian model building (as a researcher, you construct the model, it isn’t just given to you as in a textbook), model checking (breaking through the absolutely horrible attitude, common to Bayesians in the early 1990s, that the model was “subjective” and thus should not be checked), and model improvement (continuous model expansion, not the misguided idea of assigning posterior probabilities). Going beyond simple conjugate models. BDA had lots of hierarchical models, also lots of computational tools so that you could fit the models you want by putting them together from understandable components. And I like how we had a clear separation between modeling and computing. The model comes first, then you…