Programming for Mathematical Applications
The Metropolis-Hastings algorithm is a Markov Chain Monte Carlo (MCMC) method used for sampling from a probability distribution when direct sampling is challenging. It generates a sequence of samples that converge to the desired distribution, using a proposal distribution to create candidate samples and a mechanism to accept or reject them based on a calculated acceptance probability. This algorithm is essential in various fields such as Bayesian statistics, computational physics, and machine learning for performing inference and exploring complex distributions.
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