Approximate expectation maximization

Advances in Neural Information Processing Systems

Volumn , Issue , 2004, Pages

  Heskes, Tom ^a   Zoeter, Onno ^a   Wiegerinck, Wim ^a  

^a RADBOUD UNIVERSITY NIJMEGEN   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; INFERENCE ENGINES; MAXIMUM PRINCIPLE;

APPROXIMATE EM; APPROXIMATE INFERENCE; BELIEF PROPAGATION ALGORITHM; EM ALGORITHMS; EXPECTATION - MAXIMIZATIONS; EXPECTATION-MAXIMIZATION ALGORITHMS; INNER LOOPS; MAXIMUM LIKELIHOOD LEARNING;

ALGORITHMS;

EID: 84899007304     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. A. Dempster, N. Laird, and D. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society B, 39:1-38, 1977.
2. R. Neal and G. Hinton. Aview of the EM algorithm that justifies incremental, sparse, and other variants. In M. Jordan, editor, Learning in Graphical Models, pages 355-368. Kluwer Academic Publishers, Dordrecht, 1998.
3. K. Murphy, Y. Weiss, and M. Jordan. 'Loopy belief propagation for approximate inference: An empirical study. In Proceedings of the Fifteenth Conference on Uncertainty in Articial Intelligence, pages 467-475, San Francisco, CA, 1999. Morgan Kaufmann.
4. J. Yedidia, W. Freeman, and Y. Weiss. Constructing free energy approximations and generalized belief propagation algorithms. Technical report, Mitsubishi Electric Research Laboratories, 2002.
5. T. Minka. Expectation propagation for approximate Bayesian inference. In Uncertainty in Artificial Intelligence: Proceedings of the Seventeenth Conference (UAI-2001), pages 362-369, San Francisco, CA, 2001. Morgan Kaufmann Publishers.
6. B. Frey and A. Kanna. Accumulator networks: Suitors of local probability propagation. In T. Leen, T. Dietterich, and V. Tresp, editors, Advances in Neural Information Processing Systems 13, pages 486-492. MIT Press, 2001.
7. T. Minka and J. Lafferty. Expectation propagation for the generative aspect model. In Proceedings of UAI-2002, pages 352-359, 2002.
8. A. Yuille. CCCP algorithms to minimize the Bethe and Kikuchi free energies: Convergent alternatives to belief propagation. Neural Computation, 14:1691-1722, 2002.
9. Y. Teh and M. Welling. The unified propagation and scaling algorithm. In NIPS 14, 2002.
10. R. Salakhutdinov and S. Roweis. Adaptive overrelaxed bound optimization methods. In ICML-2003, 2003.
11. T. Heskes, K. Albers, and B. Kappen. Approximate inference and constrained optimization. In UAI-2003, 2003.
12. K. Murphy and Y. Weiss. The factored frontier algorithm for approximate inference in DBNs. In UAI-2001, pages 378-385, 2001.
13. M. Wainwright, T. Jaakkola, and A. WHIsky. Tree-reweighted belief propagation algorithms and approximate ML estimation via pseudo-moment matching. In AISTATS-2003, 2003.
14. Y. Teh and M. Welling. On improving the efficiency of the iterative proportional fitting procedure. In AISTATS-2003, 2003.