Mattias Villani is a Professor at the Department of Statistics at Stockholm University, Sweden. He has previously held positions at the Central bank of Sweden and Linköping University, where he built up and headed the Division of Statistics and Machine Learning. His research centers on computationally efficient methods for Bayesian inference, prediction and decision making, using flexible probabilistic models. The methods are applied to a wide range of problems in economics, neuroimaging, transportation, robotics and text analysis.
Title: An Introduction to Bayesian Learning for Uncertainty Quantification and Decision Making
Abstract: Data science and machine learning is inherently about making decisions under uncertainty. In this lecture I will explain how the Bayesian approach gives a natural quantification of uncertainty, in a form that is directly useful for decision making. I will also briefly discuss how computer simulation is used as an everyday tool for Bayesian computations, and introduce a couple of probabilistic programming languages that can be used to perform a Bayesian analysis with minimal effort.