Month: June 2019

Fundamental Bayesian Samplers

The posterior probability distribution is the heart of Bayesian statistics and a fundamental tool for Bayesian parameter estimation. Naturally, how to infer and build these distributions is a widely examined topic, the scope of which cannot fit in one blog. In this blog, we examine bayesian sampling using three basic, but fundamental techniques, importance sampling, Metropolis-Hastings sampling, and Gibbs sampling.

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