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, p(\theta | y) \propto p(y|\theta)p(\theta)

p(\theta | y) \propto p(y|\theta)p(\theta)

. Very briefly, the algorithm works by starting with some initial draw

\theta^{(0)}

then running … Continue reading Efficient MCMC with Caching

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