- Aug 30: intro, math quiz, how does statistics work?
- Sept 1: Chapter 2 – Beliefs, probability and exchangeability (hw1 assigned)
- Sept 6: Chapter 2+ – more on exchangeability
- Sept 8: Chapter 3 – Binomial and Poisson models (hw1 due, hw2 assigned)
- Sept 13: Chapter 3 – Exponential families
- Sept 15: Chapter 3+ – more priors (hw2 due, hw3 assigned)
- Sept 20: Chapter 4 – Monte Carlo
- Sept 22: Chapter 4 – model checking (hw3 due, hw4 assigned)
- Sept 27: Chapter 5 – the normal model
- Sept 29: Chapter 5 – the normal model (hw4 due)
- Oct 4: Catch up and mild review
- Oct 6: Midterm 1
- Oct 13: Chapter 6 – Gibbs samplers (hw5 assigned)
- Oct 18: Chapter 6 – Gibbs samplers and diagnostics
- Oct 20: Chapter 7 – multivariate normal (hw5 due, hw6 assigned)
- Oct 25: Chapter 7 – multivariate normal and missing data
- Oct 27: Chapter 8 – group comparisons (hw6 due, hw7 assigned)
- Nov 1: Chapter 8/9 – hierarchical modeling
- Nov 3: Chapter 9 – Bayesian estimation of the linear regression model (hw7 due, hw8 assigned)
- Nov 8: Catch up and mild review (hw8 due)
- Nov 10: Midterm 2
- Nov 15: Chapter 9 – Model selection (hw9 assigned)
- Nov 17: Chapter 10 – nonconjugate priors – Metropolis algorithm
- Nov 22: Chapter 10 – Metropolis-Hastings + Gibbs(hw9 due, hw10 assigned)
- Nov 29: Chapter 11/12 – Logistic regression
- Dec 1: Chapter 12 – ordinal/rank data (hw10 due)
- Dec 6: review 1
- Dec 8: review 2
- Dec 12: review 3
- Dec 13: review 4