posterior

Introduction In modern Bayesian statistics, we often face posterior distributions that are difficult to compute. Let $p(z)$ be prior density and $p(x\mid z)$ be likelihood. The standard approach to compute posterior $p(z\mid x)$ is to use MCMC (like Metropolis-Hastings, Gibbs sampling and HMC).

Bayesian Inference Posterior Distribution The intuition behind the posterior distribution: “It is a weighted version of likelihood! … just weighting the likelihood using my prior belief on theta …” Useful