Metropolis-Hastings Algorithm
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A Metropolis-Hastings Algorithm is an MCMC algorithm that obtain a sequence of pseudo-random number samples from a probability distribution.
- See: Adaptive Rejection Sampling, Markov Chain Monte Carlo, Pseudo-Random Number Sampling, Expected Value.
References
2015
- (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/Metropolis–Hastings_algorithm Retrieved:2015-5-2.
- 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.