Bishop, Christopher M.

Spiegelhalter, David J.

Winn, John

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

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VIBES: A Variational Inference Engine for Bayesian Networks

https://www.pure.ed.ac.uk/ws/files/24600888/2172_vibes_a_variational_inference_engine_for_bayesian_networks.pdf

VIBES: A Variational Inference Engine for Bayesian Networks

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