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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