• Aug 29: intro, math quiz, how does statistics work?
  • Aug 31: Chapter 2 – Beliefs, probability and exchangeability (hw1 assigned)
  • Sept 5: Chapter 2+ – more on exchangeability
  • Sept 7: Chapter 3 – Binomial and Poisson models (hw1 due, hw2 assigned)
  • Sept 12: Chapter 3 – Exponential families
  • Sept 14: Chapter 3+ – more priors and Monte Carlo (hw2 due, hw3 assigned)
  • Sept 19: Chapter 4 – Monte Carlo
  • Sept 21: Chapter 4 – Quiz I and model checking (hw3 due, hw4 assigned)
  • Sept 26: Chapter 5 – (finishing posterior predictive checks) the normal model
  • Sept 28: Chapter 5 – the normal model (hw4 due, hw5 assigned)
  • Oct 3: Chapter 6 – Gibbs samplers
  • Oct 5: Chapter 6 – Gibbs samplers and diagnostics (hw5 due, hw6 assigned)
  • Oct 10: No class (Fall Break)
  • Oct 12: Chapter 7 – multivariate normal
  • Oct 17: Catch up and review
  • Oct 19: Midterm
  • Oct 23: Chapter 7 – multivariate normal and missing data
  • Oct 26: Chapter 8 – group comparisons
  • Oct 31: Chapter 8/9 – hierarchical modeling
  • Nov 2: Chapter 9 – Bayesian estimation of the linear regression model
  • Nov 7: Bayesian estimation of simple generalized linear models
  • Nov 9: Chapter 9 – Model selection
  • Nov 14: Chapter 10 – nonconjugate priors – Metropolis algorithm
  • Nov 16: Quiz II and Chapter 10 – Metropolis-Hastings + Gibbs
  • Nov 21: Chapter 11/12 – Logistic regression
  • Nov 28: Chapter 11/12 – Examples
  • Nov 30: Chapter 12 – ordinal/rank data
  • Dec 5: Graduate reading period… review 1
  • Dec 7: Graduate reading period… review 2
  • Dec 12: Graduate reading period… review 3
  • Dec 17: Final exam due