Variational message passing

Journal of Machine Learning Research

Volumn 6, Issue , 2005, Pages

  Winn, John ^a   Bishop, Christopher M ^a  

^a MICROSOFT RESEARCH   (United Kingdom)

Author keywords

Bayesian networks; Message passing; Variational inference

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; GRAPH THEORY; MATHEMATICAL MODELS; MONTE CARLO METHODS; PARAMETER ESTIMATION; PROBABILITY;

BAYESIAN NETWORKS; MESSAGE PASSING; MONTE CARLO ALGORITHMS; VARIATIONAL INFERENCE;

LEARNING SYSTEMS;

EID: 21844450606     PISSN: 15337928     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (19)

1. H. Attias. A variational Bayesian framework for graphical models. In S. Solla, T. K. Leen, and K-L Muller, editors, Advances in Neural Information Processing Systems, volume 12, pages 209-215, Cambridge MA, 2000. MIT Press.
2. C. M. Bishop. Variational principal components. In Proceedings Ninth International Conference on Artificial Neural Networks, ICANN'99, volume 1, pages 509-514. IEE, 1999.
3. C. M. Bishop and M. Svensén. Bayesian Hierarchical Mixtures of Experts. In U. Kjaerulff and C. Meek, editors, Proceedings Nineteenth Conference on Uncertainty in Artificial Intelligence, pages 57-64. Morgan Kaufmann, 2003.
4. C. M. Bishop and J. M. Winn. Non-linear Bayesian image modelling. In Proceedings Sixth European Conference on Computer Vision, volume 1, pages 3-17. Springer-Verlag, 2000.
5. C. M. Bishop and J. M. Winn. Structured variational distributions in VIBES. In Proceedings Artificial Intelligence and Statistics, Key West, Florida, 2003. Society for Artificial Intelligence and Statistics.
6. C. M. Bishop, J. M. Winn, and D. Spiegelhalter. VIBES: A variational inference engine for Bayesian networks. In Advances in Neural Information Processing Systems, volume 15, 2002.
7. R. G. Cowell, A. P. Dawid, S. L. Lauritzen, and D. J. Spiegelhalter. Probabilistic Networks and Expert Systems. Statistics for Engineering and Information Science. Springer-Verlag, 1999.
8. Z. Ghahramani and M. J. Beal. Propagation algorithms for variational Bayesian learning. In T. K. Leen, T. Dietterich, and V. Tresp, editors, Advances in Neural Information Processing Systems, volume 13, Cambridge MA, 2001. MIT Press.
9. W. R. Gilks and P. Wild. Adaptive rejection sampling for Gibbs sampling. Applied Statistics, 41(2): 337-348, 1992.
10. T. Jaakkola and M. Jordan. A variational approach to Bayesian logistic regression problems and their extensions. In In Proceedings of the 6th international workshop on artificial intelligence and statistics., 1996.
11. M. I. Jordan, Z. Ghahramani, T. S. Jaakkola, and L. K. Saul. An introduction to variational methods for graphical models. In M. I. Jordan, editor, Learning in Graphical Models, pages 105-162. Kluwer, 1998.
12. S. L. Lauritzen. Propagation of probabilities, means, and variances in mixed graphical association models. Journal of the American Statistical Association, 87(420): 1098-1108, 1992.
13. D. J. Lunn, A. Thomas, N. G. Best, and D. J. Spiegelhalter. WinBUGS - a Bayesian modelling framework: concepts, structure and extensibility. Statistics and Computing, 10:321-333, 2000. http://www.mrc-bsu.cam.ac.uk/bugs/.
14. T. P. Minka. Expectation propagation for approximate Bayesian inference. In Proceedings of the 17th Annual Conference on Uncertainty in Artificial Intelligence, pages 362-369. Morgan Kauffmann, 2001.
15. R. M. Neal and G. E. Hinton. A new view of the EM algorithm that justifies incremental and other variants. In M. I. Jordan, editor, Learning in Graphical Models, pages 355-368. Kluwer, 1998.
16. J. Pearl. Fusion, propagation and structuring in belief networks. Artificial Intelligence, 29:241-288, 1986.
17. A. Thomas, D. J. Spiegelhalter, and W. R. Gilks. BUGS: A program to perform Bayesian inference using Gibbs sampling. In J. M. Bernardo, J. O. Berger, A. P. Dawid, and A. F. M. Smith, editors, Bayesian Statistics, Oxford: Clarendon Press, 1992.
18. W. Wiegerinck. Variational approximations between mean field theory and the junction tree algorithm. In Uncertainty in Artificial Intelligence. Morgan Kauffmann, 2000.
19. J. M. Winn. Variational Message Passing and its Applications. PhD thesis, University of Cambridge, October 2003.