Metropolis–Hastings algorithm  From Wikipedia, the free
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navigation, search The proposal distribution Q proposes the next
point that the random walk might move to.  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