  Deep neural networks have achieved impressive results on a wide variety of
tasks. However, quantifying uncertainty in the network's output is a
challenging task. Bayesian models offer a mathematical framework to reason
about model uncertainty. Variational methods have been used for approximating
intractable integrals that arise in Bayesian inference for neural networks. In
this report, we review the major variational inference concepts pertinent to
Bayesian neural networks and compare various approximation methods used in
literature. We also talk about the applications of variational bayes in
Reinforcement learning and continual learning.
