- Instructor: Gustavo de los Campos ( gustavoc@msu.edu )
- Time/Place: MW 10:20am-11:40am A120 Wells Hall (WH)
- Syllabus
- Required textbook
- R-software
In this course we follow closely the required textbook: "A first Course in Bayesian Statistical Methods" (P.D. Hoff).
HW
Lectures
Chapter 1: Introduction and examples
Chapter 2: Belief, probability and exchangeability
Chapter 3: One-parameter models
Chapter 4: Monte Carlo approximations
Chapter 5: The normal model
Chapter 6: Posterior approximation with the Gibbs sampler
Review of Linear Algebra & Multivariate Normal
Multiple linear regression OLS and Maximum Likelihood
The multivariate normal distribution & intro to Bayesian multiple linear regression
Chapter 10: Nonconjugate priors and Metropolis-Hastings algorithms
- Lecture
- Examples
Chapter 11: Linear and generalized linear mixed effects models
- Lecture
- Examples
Chapter 12: Latent variable methods for ordinal data
- Lecture
- Examples