Optimal Information Processing and Bayes' Theorem

by Arnold Zellner

It is really something easy. The input is the likelihood Pr(x | θ) and the prior π(θ) and the output is Pr(θ|D) and Pr(x). So the author proposes to minimize the information (log) or the KL divergence. This would lead to Bayes' theorem.

Well is this justification right?