Optimal Belief Approximation
This clarifies a foundational issue in Bayesian statistics for researchers and practitioners, though it is incremental as it reproduces and discusses an old proof.
The paper addresses confusion over the correct order of arguments in the Kullback-Leibler divergence for approximating Bayesian beliefs, showing that the optimal approximation under specific requirements leads to moment matching for Gaussian distributions, contrasting with many computational schemes.
In Bayesian statistics probability distributions express beliefs. However, for many problems the beliefs cannot be computed analytically and approximations of beliefs are needed. We seek a loss function that quantifies how "embarrassing" it is to communicate a given approximation. We reproduce and discuss an old proof showing that there is only one ranking under the requirements that (1) the best ranked approximation is the non-approximated belief and (2) that the ranking judges approximations only by their predictions for actual outcomes. The loss function that is obtained in the derivation is equal to the Kullback-Leibler divergence when normalized. This loss function is frequently used in the literature. However, there seems to be confusion about the correct order in which its functional arguments, the approximated and non-approximated beliefs, should be used. The correct order ensures that the recipient of a communication is only deprived of the minimal amount of information. We hope that the elementary derivation settles the apparent confusion. For example when approximating beliefs with Gaussian distributions the optimal approximation is given by moment matching. This is in contrast to many suggested computational schemes.