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  1. Markov chain Monte Carlo

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    Markov chain Monte Carlo (MCMC) methods comprise a class of algorithms for sampling from a probability distribution. By constructing a Markov chain that

  2. Monte Carlo method

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    mathematicians often use a Markov chain Monte Carlo (MCMC) sampler. The central idea is to design a judicious Markov chain model with a prescribed stationary

  3. Markov model

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    distribution of a previous state. An example use of a Markov chain is Markov chain Monte Carlo, which uses the Markov property to prove that a particular method

  4. Reversible-jump Markov chain Monte Carlo

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    computational statistics, reversible-jump Markov chain Monte Carlo is an extension to standard Markov chain Monte Carlo (MCMC) methodology that allows simulation

  5. Hamiltonian Monte Carlo

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    and statistics, the Hamiltonian Monte Carlo algorithm (also known as hybrid Monte Carlo), is a Markov chain Monte Carlo method for obtaining a sequence

  6. Markov chain central limit theorem

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    Geyer, Charles J. (2011). Introduction to Markov Chain Monte Carlo. In Handbook of MarkovChain Monte Carlo. Edited by S. P. Brooks, A. E. Gelman, G. L

  7. Metropolis–Hastings algorithm

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    and statistical physics, the Metropolis–Hastings algorithm is a Markov chain Monte Carlo (MCMC) method for obtaining a sequence of random samples from a

  8. Markov chain

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    is based on a Markov process. Markov processes are the basis for general stochastic simulation methods known as Markov chain Monte Carlo, which are used

  9. Markov chain mixing time

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    Markov chain is the time until the Markov chain is "close" to its steady state distribution. More precisely, a fundamental result about Markov chains

  10. Hidden Markov model

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    prediction, more sophisticated Bayesian inference methods, like Markov chain Monte Carlo (MCMC) sampling are proven to be favorable over finding a single

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