Efficient MCMC with Caching

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