Christopher M. Bishop, Microsoft Research, Cambridge, United Kingdom
Bayesian methods offer significant advantages over many conventional techniques such as maximum likelihood. However, their practicality has traditionally been limited by the computational cost of implementing them, which has often been done using Monte Carlo methods. In recent years, however, the applicability of Bayesian methods has been greatly extended through the development of fast analytical techniques such as variational inference. In this talk I will give a tutorial introduction to variational methods and will demonstrate their applicability in both supervised and unsupervised learning domains. I will also discuss techniques for automating variational inference, allowing rapid prototyping and testing of new probabilistic models.
Christopher Bishop received his Ph.D. from the University of Edinburgh in 1983. In 1993 he was appointed Professor in the Department of Computer Science and Applied Mathematics at Aston University, where he was a member of the Neural Computing Research Group. In 1998 he joined the Microsoft Research Laboratory in Cambridge. He is also Professor of Computer Science at the University of Edinburgh and a Fellow of Darwin College, Cambridge.