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Markov chain Monte Carlo
aimlexchange.com/search/wiki/page/Markov_chain_Monte_CarloMarkov chain Monte Carlo (MCMC) methods comprise a class of algorithms for sampling from a probability distribution. By constructing a Markov chain that

Monte Carlo method
aimlexchange.com/search/wiki/page/Monte_Carlo_methodmathematicians often use a Markov chain Monte Carlo (MCMC) sampler. The central idea is to design a judicious Markov chain model with a prescribed stationary

Markov model
aimlexchange.com/search/wiki/page/Markov_modeldistribution 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

Reversiblejump Markov chain Monte Carlo
aimlexchange.com/search/wiki/page/Reversiblejump_Markov_chain_Monte_Carlocomputational statistics, reversiblejump Markov chain Monte Carlo is an extension to standard Markov chain Monte Carlo (MCMC) methodology that allows simulation

Hamiltonian Monte Carlo
aimlexchange.com/search/wiki/page/Hamiltonian_Monte_Carloand statistics, the Hamiltonian Monte Carlo algorithm (also known as hybrid Monte Carlo), is a Markov chain Monte Carlo method for obtaining a sequence

Markov chain central limit theorem
aimlexchange.com/search/wiki/page/Markov_chain_central_limit_theoremGeyer, 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

Metropolis–Hastings algorithm
aimlexchange.com/search/wiki/page/Metropolis%E2%80%93Hastings_algorithmand statistical physics, the Metropolis–Hastings algorithm is a Markov chain Monte Carlo (MCMC) method for obtaining a sequence of random samples from a

Markov chain
aimlexchange.com/search/wiki/page/Markov_chainis based on a Markov process. Markov processes are the basis for general stochastic simulation methods known as Markov chain Monte Carlo, which are used

Markov chain mixing time
aimlexchange.com/search/wiki/page/Markov_chain_mixing_timeMarkov chain is the time until the Markov chain is "close" to its steady state distribution. More precisely, a fundamental result about Markov chains

Hidden Markov model
aimlexchange.com/search/wiki/page/Hidden_Markov_modelprediction, more sophisticated Bayesian inference methods, like Markov chain Monte Carlo (MCMC) sampling are proven to be favorable over finding a single