STAT 547 Bayesian Workflow
❄️ Winter 2026
Course notes
References
- Bayesian Workflow (draft) — upcoming
- Stan Documentation
Reading list
Week 2
- For how many iterations should we run MCMC? Margossian and Gelman. [pdf]
- Rank-normalization, folding, and localization: an improved R for assessing convergence of MCMC Vehtari et al. [pdf]
- Probabilistic Inference using Markov chain Monte Carlo methods Neal. Sections 3–4. [pdf]
- General State Space Markov chains and MCMC algorithms Roberts and Rosenthal. Sections 1–3. [pdf]
Week 3
- Practical Bayesian Model Evaluation using Leave-One-Out Cross-Validation and WAIC Vehtari, Gelman, and Gabry. [pdf]
- A Conceptual Introduction to Hamiltonian Monte Carlo Betancourt. [pdf]
- MCMC using Hamiltonian dynamics Neal. [pdf]
Week 4
- Adaptively Setting Path Lengths in Hamiltonian Monte Carlo Hoffman and Gelman. [pdf]
- An Adaptive MCMC Scheme for Setting Trajectory Lengths in Hamiltonian Monte Carlo Hoffman, Radul and Sountsov. [pdf]
- Nested R: Assessing the Convergence of MCMC when running many short chains Margossian et al. [pdf]
- Estimating Convergence of Markov chain Monte Carlo with L-lag Coupling Biswas, Jacob and Vanetti. [pdf]
Week 5
- Variational Inference: A Review for Statisticians Blei, Kucukelbir and McAuliffe. [pdf]
- Yes but did it work? Evaluating Variational Inference Yao et al. [pdf]
- Validated variational inference via practical posterior error bounds Huggins et al. [pdf]
- Pathfinder: Parallel quasi-Newton variational inference Zhang et al. [pdf]