This post is part of a running series on Bayesian MCMC tutorials. For updates, follow @StableMarkets. Metropolis Review Metropolis-Hastings is an MCMC algorithm for drawing samples from a distribution known up to a constant of proportionality, $latex p(\theta | y) \propto p(y|\theta)p(\theta)$. Very briefly, the algorithm works by starting with some initial draw $latex \theta^{(0)}$ then running … Continue reading Efficient MCMC with Caching

# Interactive Tutorial on Dirichlet Processes Using R Shiny

For More posts on Bayesian models, Bayesian Nonparametrics, and causal inference follow me on twitter @stablemarkets. My advisor and his collaborator are teaching a short course on Bayesian Nonparametric Methods for Causal Inference at JSM next week. As part of the short course, I made an interactive tutorial on Dirichlet Processes using R Shiny. All … Continue reading Interactive Tutorial on Dirichlet Processes Using R Shiny

# Bayesian Inference with Backfitting MCMC

Previous posts in this series on MCMC samplers for Bayesian inference (in order of publication): Bayesian Simple Linear Regression with Gibbs Sampling in R Blocked Gibbs Sampling in R for Bayesian Multiple Linear Regression Metropolis-in-Gibbs Sampling and Runtime Analysis with Profviz Speeding up Metropolis-Hastings with Rcpp All code for this (and previous) posts are in … Continue reading Bayesian Inference with Backfitting MCMC

# Speeding up Metropolis-Hastings with Rcpp

Previous posts in this series on MCMC samplers for Bayesian inference (in order of publication): Bayesian Simple Linear Regression with Gibbs Sampling in R Blocked Gibbs Sampling in R for Bayesian Multiple Linear Regression Metropolis-in-Gibbs Sampling and Runtime Analysis with Profviz The code for all of these posts can be found in my BayesianTutorials GitHub … Continue reading Speeding up Metropolis-Hastings with Rcpp

# Metropolis-in-Gibbs Sampling and Runtime Analysis with Profviz

First off, here are the previous posts in my Bayesian sampling series: Bayesian Simple Linear Regression with Gibbs Sampling in R Blocked Gibbs Sampling in R for Bayesian Multiple Linear Regression In the first post, I illustrated Gibbs Sampling - an algorithm for getting draws from a posterior when conditional posteriors are known. In the … Continue reading Metropolis-in-Gibbs Sampling and Runtime Analysis with Profviz

# Blocked Gibbs Sampling in R for Bayesian Multiple Linear Regression

In a previous post, I derived and coded a Gibbs sampler in R for estimating a simple linear regression. In this post, I will do the same for multivariate linear regression. I will derive the conditional posterior distributions necessary for the blocked Gibbs sampler. I will then code the sampler and test it using simulated … Continue reading Blocked Gibbs Sampling in R for Bayesian Multiple Linear Regression

# Bayesian Simple Linear Regression with Gibbs Sampling in R

Many introductions to Bayesian analysis use relatively simple didactic examples (e.g. making inference about the probability of success given bernoulli data). While this makes for a good introduction to Bayesian principles, the extension of these principles to regression is not straight-forward. This post will sketch out how these principles extend to simple linear regression. Along … Continue reading Bayesian Simple Linear Regression with Gibbs Sampling in R