A Bayesian Inference over Neural Networks On a supervised model parameterized by W, we seek to infer the conditional distribution W | D

The prior and likelihood are both modelling choices. A.1 Likelihoods for BNNs The likelihood is purely a function of the model prediction Φ As exact posterior inference via (11) is intractable, we instead rely on approximate inference algorithms, which can be broadly grouped into two classes based on their method of approximation. A concrete label can be obtained by choosing the class with highest output value. The Gaussian variational family is a common choice. Estimators for the integral in (15) are necessary.