Software

• The Distribution Explorer provides quick references for various distributions and the syntax for using them in Python and Stan.

• Stan is our main engine for Bayesian inference. You will refer to its documentation many times throughout the term.

• CmdStanPy if our primary interface to Stan.

• ArviZ provides valuable tools for parsing Stan results.

• The bebi103 package provides utility functions for MCMC-based inference and other useful functionality for the class.

Reading/tutorials

• Michael Betancourt’s writings contain some of the best explanations and case studies about Bayesian inference, especially with Stan, you can find anywhere.

• Bayesian Data Analysis, 3rd Ed. by Gelman, et al., is for many researchers the primary reference for Bayesian inference. The authors have made it freely available at this link.

• Stan tutorials YouTube channel has a great series of videos.

• Statistical Rethinking, 2nd Ed. by Richard McElreath is a good pedagogical book about Bayesian inference.

• A Student’s Guide to Bayesian Statistics by Ben Lambert is another good reference, especially for beginners.

• Data Modeling for the Sciences by Pressé and Sgouralis is a new book that gives a clear exposition on how Bayesian inference is used in scientific reasoning.

• Bayesian Logical Data Analysis for the Physical Sciences by Phil Gregory. There is also a supplement available. This is a nice book to discuss how Bayesian inference fits in with logical approaches to science.