SYMPOSIUM ON ADVANCES IN APPROXIMATE BAYESIAN INFERENCE, VOL 118(2019)
Google Res
被引用7|浏览45
摘要
In classic papers, Zellner 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.