- Keywords: Information Bottleneck, Bayesian Inference, Variational Bounds
- TL;DR: Rederive a wide class of inference procedures from an global information bottleneck objective.
- Abstract: In classic papers, Zellner (1988, 2002) demonstrated that Bayesian inference could be derived as the solution to an information theoretic functional. Below we derive a generalized form of this functional as a variational lower bound of a predictive information bottleneck objective. This generalized functional encompasses most modern inference procedures and suggests novel ones.