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- Q910810 subject Q7216426.
- Q910810 subject Q8609427.
- Q910810 subject Q9210903.
- Q910810 abstract "In statistics and in statistical physics, the Metropolis–Hastings algorithm is a Markov chain Monte Carlo (MCMC) method for obtaining a sequence of random samples from a probability distribution for which direct sampling is difficult. This sequence can be used to approximate the distribution (i.e., to generate a histogram), or to compute an integral (such as an expected value). Metropolis–Hastings and other MCMC algorithms are generally used for sampling from multi-dimensional distributions, especially when the number of dimensions is high. For single-dimensional distributions, other methods are usually available (e.g. adaptive rejection sampling) that can directly return independent samples from the distribution, and are free from the problem of auto-correlated samples that is inherent in MCMC methods.".
- Q910810 thumbnail Metropolis_hastings_algorithm.png?width=300.
- Q910810 wikiPageExternalLink a2rms.sourceforge.net.
- Q910810 wikiPageExternalLink r-code-for-multivariate-random-walk-metropolis-hastings-sampling.
- Q910810 wikiPageExternalLink metropolis-hastings.
- Q910810 wikiPageExternalLink .VOk8J1PF9_c.
- Q910810 wikiPageExternalLink Metropolis-Hastings+algorithm.
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- Q910810 wikiPageWikiLink Q7216426.
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- Q910810 wikiPageWikiLink Q8609427.
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- Q910810 comment "In statistics and in statistical physics, the Metropolis–Hastings algorithm is a Markov chain Monte Carlo (MCMC) method for obtaining a sequence of random samples from a probability distribution for which direct sampling is difficult. This sequence can be used to approximate the distribution (i.e., to generate a histogram), or to compute an integral (such as an expected value).".
- Q910810 label "Metropolis–Hastings algorithm".
- Q910810 depiction Metropolis_hastings_algorithm.png.