# Metropolis-Hastings algorithm

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## Summary

Markov Chain Monte Carlo (MCMC) is a method for approximately sampling from a distribution p by defining a Markov chain which has p as a stationary distribution. Metropolis-Hastings is a very general recipe for finding such a Markov chain: choose a proposal distribution and correct for the bias by stochastically accepting or rejecting the proposal. While the mathematical formalism is very general, there is an art to choosing good proposal distributions.

## Context

This concept has the prerequisites:

- Markov chain Monte Carlo (M-H is an example of an MCMC algorithm.)
- multivariate Gaussian distribution (Gaussian proposals are a canonical example of an M-H proposal distribution.)

## See also

-No Additional Notes-