The Metropolis–Hastings algorithm is a Markov chain Monte Carlo method used to obtain random samples from a probability distribution. It is mainly used for multi-dimensional distributions, while single-dimensional distributions can be sampled using other methods such as adaptive rejection sampling. These methods are free from the problem of autocorrelated samples that is inherent in MCMC methods.
Stanford University
Autumn 2022
The course addresses both classic and recent developments in counting and sampling. It covers counting complexity, exact counting via determinants, sampling via Markov chains, and high-dimensional expanders.
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