This is the home page for the book, Bayesian Data Analysis, by Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin. Here is the book in pdf form, available for download …

Non-Bayesian approaches dominated statistical theory and practice for most of the last century, but the last few decades have seen a re-emergence of Bayesian methods.

Department of Statistics - Columbia University

Apr 6, 2020 · There is so much frequentist “infrastructure” around me (both in my field, as bayesian is not common, and in my teaching that has somehow to follow the old hypothesis testing framework) …

Bayesian data analysis takes Bayesian inference as a starting point but also includes fit-ting a model to different datasets, altering a model, performing inferential and predictive summaries (in-cluding prior …

Data from examples in Bayesian Data Analysis. References to tables, figures, and pages are to the second edition of the book except where noted. We thank Kjetil Halvorsen for pointing out a typo. …

Bayesian data analysis: (3) Model checking/improvement Do the inferences make sense? Are the model's predictions consistent with the data? Not: Is the model true? Not: What is Pr (model is true)?

Jul 2, 2025 · It was also fun to write all the things in chapter 4 of BDA, including connections between Bayesian and non-Bayesian methods. I think our work here is solid, and it goes beyond the simple …

We illustrate issues of model construction and computation with a relatively complete Bayesian analysis of an educational experiment and of a meta-analysis of a set of medical studies.

exploratory data analysis and Bayesian modeling. I do not think of con idence intervals as inverses of hypothesis tests. Rather, I tend to think of maximum likelihood and other classical estimation …