Spring 2008 STA 293 : Stochastic Processes & Bayesian

Markov chains are among the simplest stochastic processes, just one step beyond iid sequences of random variables. Traditionally they’ve been used in modelling a variety of physical phenomena, but recently interest has grown enormously due to their applicability in facilitating Bayesian computation. These lecture notes and lectures are intended to introduce the elements of markov chain theory, emphasizing the convergence to stationary distributions and the resulting simulation-based numerical methods— Gibbs sampling and, more generally, Markov Chain Monte Carlo (MC).