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The true posterior distribution of a Bayesian neural network is massivelymultimodal. Whilst most of these modes are functionally equivalent, wedemonstrate that there remains a level of real multimodality that manifests ineven the simplest neural network setups. It is only by fully marginalising overall posterior modes, using appropriate Bayesian sampling tools, that we cancapture the split personalities of the network. The ability of a networktrained in this manner to reason between multiple candidate solutionsdramatically improves the generalisability of the model, a feature we contendis not consistently captured by alternative approaches to the training ofBayesian neural networks. We provide a concise minimal example of this, whichcan provide lessons and a future path forward for correctly utilising theexplainability and interpretability of Bayesian neural networks.

Split personalities in Bayesian Neural Networks: the case for full marginalisation