Speaker: François-Xavier BRIOL, University College London
Title: "Stein's Method for Computational Statistics and Machine Learning"
Abstract: Stein's method is an analytical tool developed in probability theory to control the distance between probability distributions. A central by-product of this method is the construction of function spaces whose expectation is zero against some distribution of interest. In this talk, we will demonstrate that this somewhat abstract analytical tool can be leveraged to construct practical algorithms for solving problems in computational statistics and machine learning. Examples will include the construction of deterministic approximations of Bayesian posterior distribution, of control variates for Markov chain Monte Carlo methods, and of estimators for intractable likelihood problems.
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