Metropolis-Hastings is a Markov Chain Monte Carlo (MCMC) algorithm used for obtaining a sequence of random samples from a probability distribution from which direct sampling is difficult. This technique allows for efficient exploration of complex distributions, making it a popular choice in Bayesian statistics for estimating posterior distributions. It works by generating candidate samples and accepting or rejecting them based on a specific acceptance probability, which ensures convergence to the desired target distribution.
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