@inproceedings{bishop2000non-linear,
author = {Bishop, C. M. and Winn, J. M. and Bishop, Christopher and Winn, John},
title = {Non-linear Bayesian image modelling},
booktitle = {Proceedings Sixth European Conference on Computer Vision},
year = {2000},
month = {January},
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.},
publisher = {Springer-Verlag},
url = {http://approjects.co.za/?big=en-us/research/publication/non-linear-bayesian-image-modelling/},
pages = {3–17},
volume = {1},
edition = {Proceedings Sixth European Conference on Computer Vision},
note = {Winner of ECCV 2000 Best Paper Prize},
}