Markov Chain Monte Carlo (MCMC) is a class of algorithms used for sampling from probability distributions based on constructing a Markov chain. The main characteristic of MCMC is its ability to generate samples from complex, high-dimensional distributions without requiring explicit knowledge of the distribution's form. MCMC methods are widely used in Bayesian statistics, where they help in estimating posterior distributions when analytical solutions are intractable. Common use cases include parameter estimation in statistical models, machine learning applications, and computational biology for tasks such as phylogenetic analysis.
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