  Bayesian neural networks (BNNs) with latent variables are probabilistic
models which can automatically identify complex stochastic patterns in the
data. We describe and study in these models a decomposition of predictive
uncertainty into its epistemic and aleatoric components. First, we show how
such a decomposition arises naturally in a Bayesian active learning scenario by
following an information theoretic approach. Second, we use a similar
decomposition to develop a novel risk sensitive objective for safe
reinforcement learning (RL). This objective minimizes the effect of model bias
in environments whose stochastic dynamics are described by BNNs with latent
variables. Our experiments illustrate the usefulness of the resulting
decomposition in active learning and safe RL settings.
