Bayesian Tutorials

This repo hosts code behind the series of posts that walk through MCMC implementations for various classes of Bayesian models. I wrote these in grad school as I was teaching myself Bayesian computation. I never felt like I truly understood something until I could implement it from scratch. The code is probably all very inefficient and perhaps even wrong, but is a kind of diary of my "from scratch" journey learning Bayes.

In order of publication:

1. Bayesian Simple Linear Regression with Gibbs
2. Blocked Gibbs for Bayesian Multivariate Linear Regression
3. Metropolis Hastings-in-Gibbs Sampler for Bayesian Logistic Regression
4. Using Rcpp to speed up Metropolis-Hastings
5. Bayesian Inference with Backfitting MCMC
6. Efficient MCMC with Caching