STAT 4010 - Bayesian Learning

Description

This course introduces the basic Bayesian inference procedures and philosophy, emphasizing both conceptual foundations and implementation. It covers conjugate families of distributions, Bayesian credible region, Jeffery’s prior, Markov Chain Monte Carlo, Bayes factor, Bayesian information criterion, imputation, Bayesian linear-regression models, model averaging, hierarchical models and empirical Bayes models. Hands-on implementation of estimation and inference procedures in R will be demonstrated in interactive sections. 

Prerequisites 

No prerequisite course, but probability, statistics, and programming knowledge at the level of Stat 2001, 2005, and 2006 is highly recommended.

Textbooks 

A self-contained lecture note is the main source of reference. Complementary textbooks include 

• • • (Major) Hoff, P.D. (2009). A First Course in Bayesian Statistical Methods. Springer. (Free online access via CUHK library) 

    • (Minor) Wasserman, L. (2004). All of Statistics: A Concise Course in Statistical Inference. Springer. 

    • (Minor) Albert, J. (2007). Bayesian Computation with R. Springer. 

Learning outcomes

Upon finishing the course, students are expected to 

• • 1. distinguish the difference between frequentist and Bayesian methods, and identify their pros and cons; 

    2. derive posterior distribution from commonly-used prior and sampling distributions;

    3. perform Markov chain Monte Carlo for Bayesian inference in R;

    4. build simple Bayesian models for solving real problems; and 

    5. select appropriate Bayesian tools for different statistical tasks, e.g., estimation, testing, model selection, prediction, etc.