- Aug 28: intro, math quiz, how does statistics work?
- Aug 30: Chapter 2 – Beliefs, probability and exchangeability (hw1 assigned)
- Sept 4: Chapter 2+ – more on exchangeability
- Sept 6: Chapter 3 – Binomial and Poisson models (hw1 due, hw2 assigned)
- Sept 11: Chapter 3 – Exponential families
- Sept 13: [Chapter 3+] – NO CLASS (hw2 due)
- Sept 18: [Chapter 3+] – more priors (hw3 assigned)
- Sept 20: Chapter 4 – Sampling
- Sept 25: Chapter 4 – (posterior predictive checks) the normal model (hw3 due, hw4 assigned)
- Sept 27: Chapter 5 – the normal model
- Oct 2: [Chapter 6] – Gibbs samplers (hw4 due, hw5 assigned)
- Oct 4: Catch up and review
- Oct 9: No class (Fall Break)
- Oct 11: Midterm (hw5 due—includes Midterm review)
- Oct 16: [Chapter 6] – Gibbs samplers and diagnostics (hw6 assigned)
- Oct 18: Chapter 7 – multivariate normal
- Oct 23: Chapter 7 – multivariate normal and missing data
- Oct 25: Chapter 8 – group comparisons
- Oct 30: Chapter 8/9 – hierarchical modeling
- Nov 1: Chapter 9 – Bayesian estimation of the linear regression model
- Nov 6: Bayesian estimation of simple generalized linear models
- Nov 8: Chapter 9 – Model selection
- Nov 13: Chapter 10 – nonconjugate priors – Metropolis algorithm
- Nov 15: Quiz II and Chapter 10 – Metropolis-Hastings + Gibbs
- Nov 20: Chapter 11/12 – Logistic regression
- Nov 27: Chapter 11/12 – Examples
- Nov 29: Chapter 12 – ordinal/rank data
- Dec 4: Graduate reading period… review 1
- Dec 6: Graduate reading period… review 2
- Dec 11: Graduate reading period… review 3
- Dec 14: Final exam due