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Gibbs sampling - Wikipedia

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In statistics, Gibbs sampling or a Gibbs sampler is a Markov chain Monte Carlo (MCMC) algorithm for sampling from a specified multivariate probability distribution when direct sampling from the joint distribution is difficult, but sampling from the conditional distribution is more practical. This sequence can be used to approximate the joint distribution (e.g., to generate a histogram of the distribution); to approximate the marginal distribution of one of the variables, or some subset of the variables (for example, the unknown parameters or latent variables); or to compute an integral (such as the expected value of one of the variables). Typically, some of the variables correspond to observations whose values are known, and hence do not need to be sampled.

Gibbs sampling - Wikipedia Jump to content From Wikipedia, the free encyclopedia Monte Carlo algorithm "Heat bath algorithm" redirects here. For the thermodynamic system, see Heat bath . Part of a series on Bayesian statistics Posterior = Likelihood × Prior ÷ Evidence Background Bayesian inference Bayesian probability Bayes' theorem Bernstein–von Mises theorem Coherence Cox's theorem Cromwell's rule Likelihood principle Principle of indifference Principle of maximum entropy Model building Conjugate prior Linear regression Empirical Bayes Hierarchical model Posterior approximation Markov chain

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