We investigate the general properties of general Bayesian learning, where “general Bayesian learning” means inferring a state from another that is regarded as evidence, and where the inference is conditionalizing the evidence using the conditional expectation determined by a reference probability measure representing the background subjective degrees of belief of a Bayesian Agent performing the...

Recurrent Neural Networks (RNNs) have seen a massive surge in popularity in recent years, particularly with the advent of modern architectures such as LSTMs. These sophisticated modern models have resulted in significant performance gains across a number of challenging tasks. Despite their success, we still struggle to provide a rigorous theoretical analysis of these models, or to truly underst...

Bayes factors provide a symmetricalmeasure of evidence for onemodel versus another (e.g. H1 versusH0) in order to relate theory to data. These properties help solve some (but not all) of the problems underlying the credibility crisis in psychology. The symmetry of the measure of evidence means that there can be evidence for H0 just as much as for H1; or the Bayes factor may indicate insufficien...