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Markov chain Monte Carlo (MCMC) algorithms, such as the Metropolis Algorithm and the Gibbs Sampler, are an extremely useful and popular method of approximately sampling from complicated probability distributions. Adaptive MCMC attempts to automatically modify the algorithm while it runs, to improve its performance on the fly. However, such adaptation often destroys the ergodicity properties necessary for the algorithm to be valid. In this talk, we first illustrate MCMC algorithms using simple graphical examples. We then discuss adaptive MCMC, and present examples and theorems concerning its ergodicity and efficiency.

Jeffrey Rosenthal is a professor of statistics, specialising in Markov chain Monte Carlo (MCMC) algorithms. He received his bachelor degree from the University of Toronto, and his PhD in Mathematics from Harvard University. He was awarded the 2006 CRM-SSC Prize, the 2007 COPSS Presidents’ Award, the 2013 SSC Gold Medal, and teaching awards at both Harvard and Toronto. He is a fellow of the Institute of Mathematical Statistics and of the Royal Society of Canada. His book for the general public, Struck by Lightning, was published in sixteen editions and ten languages, and was a bestseller in Canada.

Meeting ID: 924 5390 4989

Passcode: 690084