  Bayesian neural networks perform variational inference over the weights
however calculation of the posterior distribution remains a challenge. Our work
builds on variational inference techniques for bayesian neural networks using
the original Evidence Lower Bound. In this paper, we present a stochastic
bayesian neural network in which we maximize Evidence Lower Bound using a new
objective function which we name as Stochastic Evidence Lower Bound. We
evaluate our network on 5 publicly available UCI datasets using test RMSE and
log likelihood as the evaluation metrics. We demonstrate that our work not only
beats the previous state of the art algorithms but is also scalable to larger
datasets.
