Christopher Bishop, David Spiegelhalter, John Winn
In recent years variational methods have become a popular tool for approximate inference and learning in a wide variety of proba- bilistic models. For each new application, however, it is currently necessary (cid:12)rst to derive the variational update equations, and then to implement them in application-speci(cid:12)c 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 Infer- ence for Bayesian Networks’) which allows a wide variety of proba- bilistic models to be implemented and solved variationally without recourse to coding. New models are speci(cid:12)ed either through a simple script or via a graphical interface analogous to a drawing package. VIBES then automatically generates and solves the vari- ational equations. We illustrate the power and (cid:13)exibility of VIBES using examples from Bayesian mixture modelling.