Course Introduction
This advanced course covers Bayesian statistical methods for data analysis, including prior specification, posterior computation, and model checking using modern computational techniques.
Course Content
- Week 1: Foundations of Bayesian Inference
- Week 2: Conjugate Priors and Computational Methods
- Week 3: Markov Chain Monte Carlo (MCMC)
- Week 4: Hierarchical Models
- Week 5: Model Comparison and Selection
- Week 6: Bayesian Regression
- Week 7: Bayesian Nonparametrics
- Week 8: Advanced Topics and Applications
Assignments
Assignment 1: Bayesian Inference Basics
Implement basic Bayesian inference for simple models.
Download DatasetAssignment 2: MCMC Implementation
Implement and analyze MCMC algorithms for posterior sampling.
Download DatasetAssignment 3: Hierarchical Modeling
Build and analyze hierarchical Bayesian models.
Download DatasetCase Studies
Clinical Trial Analysis
Apply Bayesian methods to analyze clinical trial data with small sample sizes.
Sports Analytics
Use hierarchical models to analyze player performance data.
Datasets
Recommended Textbooks
- "Bayesian Data Analysis" by Andrew Gelman
- "Doing Bayesian Data Analysis" by John Kruschke
- "Bayesian Methods for Hackers" by Cameron Davidson-Pilon