The talk will introduce Markov chain Monte Carlo methods as a means of sampling from a distribution. The Metropolis-Hastings algorithm will be discussed as well as applications of Markov chain Monte Carlo for Bayesian inference and optimization.

Will expect familiarity with basic probability theory, especially conditional probability.

The slides for this talk are available at Jacob Jackson’s website.