  The radio astronomy community is rapidly 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 such deep learning models and will
play an important role in extracting well-calibrated uncertainty estimates on
their outputs. In this work, we evaluate the performance of different BNNs
against the following criteria: predictive performance, uncertainty calibration
and distribution-shift detection for the radio galaxy classification problem.
