This is a collection of my code from the class Bayesian and Modern Statistics (STA 360/601) during Fall 2015. The class covered Bayesian concepts such as decision theory, hypothesis testing, regression (ordinary and penalized), Gibbs and Metropolis-Hasting sampling, hierarchical models, mixed effects models, missing data imputation, and data augmentation.

The assignments consisted of weekly homework and labs. For each assignment, I have included a problem description, code (mainly Gibbs and Metropolis-Hastings samplers), and a writeup. The numberings of some labs are off in the titles of the problem descriptions, but the names of the files are accurate.