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On the other hand, most simple [[rejection sampling]] methods suffer from the "[[curse of dimensionality]]", where the probability of rejection increases exponentially as a function of the number of dimensions. Metropolis–Hastings, along with other MCMC methods, do not have this problem to such a degree, and thus are often the only solutions available when the number of dimensions of the distribution to be sampled is high. As a result, MCMC methods are often the methods of choice for producing samples from [[hierarchical Bayesian model]]s and other high-dimensional statistical models used nowadays in many disciplines.
In [[multivariate distribution|multivariate]] distributions, the classic Metropolis–Hastings algorithm as described above involves choosing a new multi-dimensional sample point. When the number of dimensions is high, finding the suitable jumping distribution to use can be difficult, as the different individual dimensions behave in very different ways, and the jumping width (see above) must be "just right" for all dimensions at once to avoid excessively slow mixing. An alternative approach that often works better in such situations, known as [[Gibbs sampling]], involves choosing a new sample for each dimension separately from the others, rather than choosing a sample for all dimensions at once. This is especially applicable when the multivariate distribution is composed of a set of individual [[random variable]]s in which each variable is conditioned on only a small number of other variables, as is the case in most typical [[hierarchical Bayesian model|hierarchical models]]. The individual variables are then sampled one at a time, with each variable conditioned on the most recent values of all the others. Various algorithms can be used to choose these individual samples, depending on the exact form of the multivariate distribution: some possibilities are the [[adaptive rejection sampling]] methods,<ref name=":0" /
==Formal derivation==
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* [https://web.archive.org/web/20110405024000/http://www.quantiphile.com/2010/11/01/metropolis-hastings/ Matlab implementation of Random-Walk Metropolis]
* [http://blog.abhranil.net/2014/02/08/r-code-for-multivariate-random-walk-metropolis-hastings-sampling/ R implementation of Random-Walk Metropolis]
* [https://github.com/kirill77/SimpleMetropolisCheck unbiased Metropolis sampling] Simple Visual C++ project which showcases numerical integration using Metropolis sampling without burn-in samples and without bias. Uses idea from Ph.D. thesis of Eric Veach "ROBUST MONTE CARLO METHODS FOR LIGHT TRANSPORT SIMULATION"
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