  Estimating how uncertain an AI system is in its predictions is important to
improve the safety of such systems. Uncertainty in predictive can result from
uncertainty in model parameters, irreducible data uncertainty and uncertainty
due to distributional mismatch between the test and training data
distributions. Different actions might be taken depending on the source of the
uncertainty so it is important to be able to distinguish between them.
Recently, baseline tasks and metrics have been defined and several practical
methods to estimate uncertainty developed. These methods, however, attempt to
model uncertainty due to distributional mismatch either implicitly through
model uncertainty or as data uncertainty. This work proposes a new framework
for modeling predictive uncertainty called Prior Networks (PNs) which
explicitly models distributional uncertainty. PNs do this by parameterizing a
prior distribution over predictive distributions. This work focuses on
uncertainty for classification and evaluates PNs on the tasks of identifying
out-of-distribution (OOD) samples and detecting misclassification on the MNIST
dataset, where they are found to outperform previous methods. Experiments on
synthetic and MNIST and CIFAR-10 data show that unlike previous non-Bayesian
methods PNs are able to distinguish between data and distributional
uncertainty.
