An Explanatory Rationale for Priors Sharpened Into Occam’s Razors

Abstract

. In Bayesian statistics, if the distribution of the data is unknown, then each plausible distribution of the data is indexed by a parameter value, and the prior distribution of the parameter is specified. To the extent that more compli-cated data distributions tend to require more coincidences for their construction than simpler data distributions, default prior distributions should be transformed to assign additional prior probability or probability density to the parameter values that refer to simpler data distributions. The proposed transformation of the prior distribution relies on the entropy of each data distribution as the relevant measure of complexity. The transformation is derived from a few first principles and extended to stochastic processes.