  Bayesian Neural Networks (BNNs) place priors over the parameters in a neural
network. Inference in BNNs, however, is difficult; all inference methods for
BNNs are approximate. In this work, we empirically compare the quality of
predictive uncertainty estimates for 10 common inference methods on both
regression and classification tasks. Our experiments demonstrate that commonly
used metrics (e.g. test log-likelihood) can be misleading. Our experiments also
indicate that inference innovations designed to capture structure in the
posterior do not necessarily produce high quality posterior approximations.
