This talk investigates the general properties of general Bayesian learning, where “general Bayesian learning” means inferring a probability measure from another that is regarded as (uncertain) 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 (prior) of a Bayesian Agent performing the inference.