Useful Links

A collection of links to documentation, tutorials, videos, and other helpful material for those interested in learning about Bayesian statistics.

1. Docs

• Stan website
• The Stan Forums (Stan developers and users usually very responsive here)
• Stan documentation (links to various kinds of documentation for Stan):
  • Stan Modeling Language User’s Guide and Reference Manual
  • Case Studies

2. Education material

• The excellent book Statistical Rethinking by Richard McElreath
• The corresponding, similarly excellent lecture videos by Richard McElreath
• Stan developer Ben Goodrich’s lecture videos and materials from his masters-level course at Columbia Bayesian Statistics for the Social Sciences
• Past Stan conferences, including videos, slides, and code (stancon_talks repository)

3. MCMC Sampling and Hamiltonian Monte Carlo (HMC)

• A Conceptual Introduction to Hamiltonian Monte Carlo (paper)
• A great Markov-chain Monte Carlo Interactive Gallery (website). Helps a lot in visualizing how these algorithms work

4. Bayesian Workflow

• Big Bayesian workflow paper by Gelman and many others link
• Visualization in Bayesian Workflow (paper, code)
• Jim Savage’s A quick-start introduction to Stan for economists is a good guide to Bayesian data analysis workflow regardless of whether or not you care about economics
• Jim Savage’s blog post Building useful models for industry—some tips
• Jeff Arnold’s notes on heteroscedasticity, with RStan examples

5. Diagnostics and reparameterization

• Visual MCMC diagnostics (tutorial vignette)
• Diagnosing biased inference with divergences (case study)
• A few simple reparameterizations (blog post)
• A bag of tips and tricks for dealing with scale issues (blog post/case study)