Gibbs sampling is a Markov Chain Monte Carlo (MCMC) algorithm used for generating samples from the joint distribution of a set of random variables, especially when direct sampling is complex or infeasible. It is particularly popular in Bayesian statistics, where it's used to perform posterior inference. ### Key Concepts of Gibbs Sampling: 1. **Goal**: The main purpose of Gibbs sampling is to approximate the joint distribution of multiple variables.
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