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Markov Chain Monte Carlo (MCMC) ○꠹｜Definition｜1st｜20251119205401-00-⌔
Markov chain Monte Carlo - Wikipedia
Markov chain Monte Carlo
In statistics, Markov chain Monte Carlo (MCMC) is a class of algorithms used to draw samples from a probability distribution. Given a probability distribution, one can construct a Markov chain whose elements’distribution approximates it, i.e. the Markov chain’s equilibrium distribution matches the target distribution. The more steps that are included, the more closely the distribution of the sample matches the actual desired distribution.
Markov chain Monte Carlo methods are used to study probability distributions that are too complex or too high dimensional to study with analytic techniques alone. Various algorithms exist for constructing such Markov chains, including the Metropolis–Hastings algorithm.
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