Information optimality and Bayesian modelling

The general approach of treating a statistical problem as one of information processing led to the Bayesian method of moments, reference priors, minimal information likelihoods, and stochastic complexity. These techniques rest on quantities that have physical interpretations from information theory. Current work includes: the role of prediction, the emergence of data dependent priors, the role of information measures in model selection, and the use of conditional mutual information to incorporate partial information.