Hybrid message passing for mixed bayesian networks

FUSION 2007 - 2007 10th International Conference on Information Fusion

Volumn , Issue , 2007, Pages

  Sun, Wei ^a   Chang, K C ^a  

^a Krasnow Institute for Advanced Study   (United States)

Author keywords

Bayesian networks; Hybrid network; Interface node; Message passing

Indexed keywords

CLUSTER ANALYSIS; DATA FUSION; DISTRIBUTED PARAMETER NETWORKS; FUSION REACTIONS; HYBRID SENSORS; INFERENCE ENGINES; INFORMATION FUSION; INTELLIGENT NETWORKS; MESSAGE PASSING; NUCLEAR PHYSICS; RANDOM VARIABLES; SENSOR DATA FUSION; SPEECH ANALYSIS;

APPROXIMATE SOLUTIONS; DISCRETE VARIABLES; EXACT INFERENCE; HIDDEN VARIABLES; HYBRID BAYESIAN NETWORKS; HYBRID MODELLING; HYBRID NETWORK; INTERFACE NODE; INTERNATIONAL CONFERENCES; MESSAGE-PASSING ALGORITHMS; MULTIPLE PATHS; NUMERICAL EXPERIMENTS; POSTERIOR DISTRIBUTIONS; SUB NETWORKS;

BAYESIAN NETWORKS;

EID: 50149100908     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIF.2007.4408140     Document Type: Conference Paper

References (16)

1. Yaakov Bar-Shalom, X. R. Li, and T. Kirubarajan. Estimation with Applications to Tracking and Navigation, Wiley, 2001.
2. I. Beinlich, G. Suermondt, R. Chavez, and G. Cooper. The alarm monitoring system: A case study with two probabilistic inference techniques for belief networks. In Proceeding of 2nd European Conference on AI and Medicine, 1989.
3. G. F. Cooper. The computational complexity of probabilistic inference using Bayesian belief networks. Artificial Intelligence, vol. 42, pp. 393-405, 1990.
4. P. Dagum and M. Luby. Approximating probabilistic inference in Bayesian belief networks is NP-hard.Artificial Intelligence, vol. 60, pp. 141-153, 1993.
5. R. Fung and K. C. Chang. Weighting and integrating evidence for stochastic simulation in Bayesian networks. In Uncertainty in Artificial Intelligence 5,pages 209-219, New York, N. Y., 1989. Elsevier Science Publishing Company, Inc., 1989.
6. Steffen L. Lauritzen. Propagation of probabilities, means, and variances in mixed graphical association models. JASA, 87(420):1089-1108, 1992.
7. S. L. Lauritzen and F. Jensen. Stable local computations with conditional Gaussian distributions. Statistics and Computing, vol.11, no.2, pp191-203, April 2001.
8. S.L. Lauritzen and D.J. Spiegelhalter, Local Computations with Probabilities on Graphical Structures and Their Applications to Expert Systems, Proceedings of the Royal Statistical Society, Series B., 50, 157-224, 1988.
9. K. Murphy, Y. Weiss, and M. Jordan. Loopy belief propagation for approximate inference:an empirical study. In UAI, 1999.
10. J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kauffman, San Mateo, 1988.
11. Wei Sun and K.C. Chang, Unscented Message Passing for Arbitrary Continuous Bayesian Networks. To be appear in the proceedings of AAAI Conference 2007.
12. Wei Sun and K.C. Chang, Hybrid Loopy Importance Sampling for Bayesian Networks. To be submitted.
13. R. D. Shachter and M. A. Peot, Simulation approaches to general probabilistic inference on belief networks. In Proc. of the Conf. on Uncertainty in AI,volume 5, 1990.
14. P. P. Shenoy and G. R. Shafer. Axioms for probability and belief-function propagation. In Proc. UAI, volume 4, pages 169-198, 1990.
15. Weiss, Y. and W. T. Freeman. Correctness of Belief Propagation in Gaussian Graphical Models of Arbitrary Topology. Technical Report, UCB.CSD-99-1046, Berkeley Computer Science Dept. 1999.
16. C. Yuan, M. J. Druzdzel, Hybrid Loopy Belief Propagation. Proceedings of the Third European Workshop on Probabilistic Graphical Models (PGM-06), pages 317-324, Milan Studeny and Jiri Vomlel (eds.), Prague: Action M Agency, 2006.