Neural-network Modelling of Bayesian Learning and Inference

We propose a modular neural-network structure for imple-menting the Bayesian framework for learning and inference. Our design has three main components, two for computing the priors and likelihoods based on observations and one for apply-ing Bayes ’ rule. Through comprehensive simulations we show that our proposed model succeeds in implementing Bayesian learning and inference. We also provide a novel explanation of base-rate neglect, the most well-documented deviation from Bayes ’ rule, by modelling it as a weight decay mechanism which increases entropy