VIBES: A Variational Inference Engine for Bayesian Networks

Christopher M. Bishop, David J. Spiegelhalter, John Winn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In recent years variational methods have become a popular tool for approximate inference and learning in a wide variety of probabilistic models. For each new application, however, it is currently necessary first to derive the variational update equations, and then to implement them in application-specific code. Each of these steps is both time consuming and error prone. In this paper we describe a general purpose inference engine called VIBES (‘Variational Inference for Bayesian Networks’) which allows a wide variety of probabilistic models to be implemented and solved variationally without recourse to coding. New models are specified either through a simple script or via a graphical interface analogous to a drawing package. VIBES then automatically generates and solves the variational equations. We illustrate the power and flexibility of VIBES using examples from Bayesian mixture modelling.
Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 15 (NIPS 2002)
Number of pages8
Publication statusPublished - 2002

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