The Fundamental Theorem of Perfect Simulation

Perfect simulation algorithms give a method for sampling exactly from high dimensional distributions. With applications both in Bayesian and Frequentist Statistics, Computer Science approximation algorithms, and statistical physics, several protocols for creating such algorithms exist. In this talk I will explore the basic principle of probabilistic recursion that underlies these different algorithms, and show how the Fundamental Theorem of Perfect Simulation can be used as a tool for building more complex methods.

Academic Bio

Mark Huber received his Ph.D. in Operations Research from Cornell University working in the area of perfect simulation. After completing a two-year postdoc with Persi Diaconis at Stanford, he begin a stint at Duke, where he received an NSF Early Career Award. Huber then moved to the Department of Mathematical Sciences at Claremont McKenna College, where he is the Fletcher Jones Foundation Associate Professor of Mathematics and Statistics, and Robert S. Day Fellow. Currently he is also the chair of the department.