title: Divergence measures and a general framework for local variational approximation.
abstract: The local variational method is a technique to approximate an intractable posterior distribution in Bayesian learning. This article formulates a general framework for local variational approximation and shows that its objective function is decomposable into the sum of the Kullback information and the expected Bregman divergence from the approximating posterior distribution to the Bayesian posterior distribution. Based on a geometrical argument in the space of approximating posteriors, we propose an efficient method to evaluate an upper bound of the marginal likelihood. Moreover, we demonstrate that the variational Bayesian approach for the latent variable models can be viewed as a special case of this general framework.
authors: Graduate School of Information Science, Nara Institute of Science and Technology, Takayama-cho, Ikoma, Nara, Japan. wkazuho@is.naist.jp
authors: Kazuho
authors: K
authors: Watanabe
authors: Masato
authors: M
authors: Okada
authors: Kazushi
authors: K
authors: Ikeda
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D016015:Logistic Models; D016571:Neural Networks, Computer; D016011:Normal Distribution; D012044:Regression Analysis