Bayesian computation: why/when Variational Bayes, not MCMC or SMC?
Bayesian inference has been increasingly used in statistics and related areas as a principled and convenient tool for reasoning with uncertainty. Bayesian computation is often a challenging task and modern applications of Bayesian inference, such as Bayesian deep learning, have been called for more scalable Bayesian computation techniques. In this talk, we will give a quick introduction to Variational Bayes for scalable Bayesian inference. We then provide a general discussion on its pros and cons, recent advances and some potential research directions.