A class of algorithms (Metropolis-Hastings, Gibbs sampling) that generate samples from an arbitrary target distribution — typically a Bayesian posterior — by constructing a Markov chain whose stationary distribution is the target. The dominant approach to approximate posterior inference when the posterior lacks a closed form.