Description
In this class, we aim to give an overview of approximate inference methods for probabilistic models. After motivating with a few succesfull Bayesian models, we introduce variational approximation methods, and then cover a few sampling algorithms leading to the Markov-Chain Monte Carlo (MCMC) methods.Slides
Slides for this lecture: PDF.Suggested Reading
A list of relevant references is given in slide #3 in the above PDF.