  The radio astronomy community is adopting deep learning techniques to deal
with the huge data volumes expected from the next-generation of radio
observatories. Bayesian neural networks (BNNs) provide a principled way to
model uncertainty in the predictions made by deep learning models and will play
an important role in extracting well-calibrated uncertainty estimates from the
outputs of these models. However, most commonly used approximate Bayesian
inference techniques such as variational inference and MCMC-based algorithms
experience a "cold posterior effect (CPE)", according to which the posterior
must be down-weighted in order to get good predictive performance. The CPE has
been linked to several factors such as data augmentation or dataset curation
leading to a misspecified likelihood and prior misspecification. In this work
we use MCMC sampling to show that a Gaussian parametric family is a poor
variational approximation to the true posterior and gives rise to the CPE
previously observed in morphological classification of radio galaxies using
variational inference based BNNs.
