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Monte-Carlo methods using Markov chains or MCMC methods for Markov chain Monte Carlo belong to a class of methods for sampling from probability distributions.

These Monte-Carlo methods are based on the traversal of Markov chains whose stationary laws are the distributions to be sampled.

Some methods use random walks on Markov chains while other more complex algorithms introduce constraints on the routes to try to accelerate convergence.

These methods are notably applied in the context of Bayesian inference.

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