Bayesian Statistical Methods (Fall 2023)

Course Schedule

Week Date Topics Lecture Reading
1 9/5 Introduction & History of Bayes Theorem Slide
2 9/12 One-parameter Models; Conjugate Priors Slide Hoff Ch. 1-3, BC Ch. 1
3 9/19 Prior Information and Prior Distribution Slide BC Ch. 3
4 9/26 Decision Theory and Bayesian Estimation Slide BC Ch. 2, 4
5 10/3 Connections to non-Bayesian Analysis; Hierarchical Models Slide BDA Ch. 4, 5
6 10/10 No class (National Holiday)
7 10/17 Testing and Model Comparison Slide BC Ch. 5, 7, BDA Ch. 6, 7
8 10/24 Project Proposal
9 10/31 Metropolis-Hastings algorithms; Gibbs sampler Slide BDA Ch. 10-11
10 11/7 Hamiltonian Monte Carlo; Variational Inference Slide BDA Ch. 12-13
11 11/14 Bayesian regression Slide BDA Ch. 14
12 11/21 Generalized Linear Models; Latent Variable Model Slide Survey BDA Ch. 16, 18
13 11/28 Gaussian Processes Slide BDA Ch. 20, 21
14 12/5 Dirichlet Processes Slide BDA Ch. 22, 23
15 12/12 Final Project Presentation
16 12/19 Final Project Presentation

Important Dates:

  • 10/10: No class (National Holiday)
  • 10/24: Project proposal: you need to prepare a 10-min presentation for your project proposal
  • 12/12,19: Final project presentation: a 20-min presentation for your final project