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CMPS Symposium Series: Adaptive MCMC for everyone
January 13, 2021 at 5:00 pm - 6:00 pm
CMPS Symposium Series: Adaptive MCMC for everyone
About the talk:
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. The talk will be followed by a Q&A session.
About the speaker:
Dr. Rosenthal is a professor of statistics at the University of Toronto. He is well known as a researcher for his contributions to Markov chain theory and for his work on improving convergence of MCMC (Markov Chain Monte Carlo) algorithms, with over 13000 Google Scholar citations.
Event details:
Wednesday, January 13 at 5 p.m. on Zoom.
Those interested in attending are asked to email Lindsay Howe, Communications and Events Coordinator at lindsay.howe@ubc.ca for the Zoom link.
