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Distinguished Lecture Series Jeffrey Rosenthal Room: DC 1302 Watch the recording on Zoom - use password SASreplay1! |
Speeding up Metropolis using Theorems
Markov chain Monte Carlo (MCMC) algorithms, such as the Metropolis algorithm, are designed to converge to complicated high-dimensional target distributions, to facilitate sampling. The speed of this convergence is essential for practical use. In this talk, we will present several theoretical results which can help improve the Metropolis algorithm's convergence speed. Specific topics will include: diffusion limits, optimal scaling, optimal proposal shape, tempering, adaptive MCMC, the Containment property, and the notion of adversarial Markov chains. The ideas will be illustrated using the simple graphical example available at probability.ca/met. No particular background knowledge will be assumed.
Jeffrey Rosenthal
Jeffrey Rosenthal is a professor of Statistics at the University of Toronto, specialising in Markov chain Monte Carlo (MCMC) algorithms. He received his BSc 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 fellowship of the Institute of Mathematical Statistics and of the Royal Society of Canada. He has published over one hundred research papers and five books (including the bestseller Struck by Lightning: The Curious World of Probabilities). Learn more on his web site.
David A. Sprott (1930-2013)
Professor David Sprott was the first Chair (1967-1975) of the Department of Statistics and Actuarial Science at the University of Waterloo and first Dean of the Faculty of Mathematics (1967-1972). The David Sprott Distinguished Lecture Series was created in recognition of his tremendous leadership at a formative time of our department, as well as his highly influential research in statistical science.