Node level primitives for parallel exact inference

Proceedings - Symposium on Computer Architecture and High Performance Computing

Volumn , Issue , 2007, Pages 221-228

  Yinglong, Xia ^a   Prasanna, Viktor K ^a  

^a University of Southern California   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COMPLEXITY; COMPUTER ARCHITECTURE; COMPUTER SCIENCE; COMPUTER SYSTEMS; COMPUTERS; DISTRIBUTED PARAMETER NETWORKS; HIGH PERFORMANCE LIQUID CHROMATOGRAPHY; INFERENCE ENGINES; INTELLIGENT NETWORKS; NETWORK ARCHITECTURE; PARALLEL ALGORITHMS; PROBABILITY; SPEECH ANALYSIS; SPEECH RECOGNITION;

BAYESIAN; EXACT INFERENCE; HIGH PERFORMANCE COMPUTING (HIPC); INTERNATIONAL SYMPOSIUM; MARGINALIZATION;

BAYESIAN NETWORKS;

EID: 47249087744     PISSN: 15506533     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SBAC-PAD.2007.31     Document Type: Conference Paper

References (13)

1. The Bayesian Network Toolbox for Matlab (BNT). http://bnt.sourceforge. net/.
2. Datastar Cluster at the San Diego Supercomputer Center (SDSC). http://www.sdsc.edu/us/resources/datastar/.
3. USC center for High-Performance Computing and Communications (HPCC). http://www.usc.edu/hpcc/.
4. D. Heckerman. Bayesian networks for data mining. In In Data Mining and Knowledge Discovery, 1997.
5. A. V. Kozlov and J. P. Singh. A parallel lauritzenspiegelhalter algorithm for probabilistic inference. In Supercomputing, pages 320-329, 1994.
6. S. L. Lauritzen and D. J. Spiegelhalter. Local computation with probabilities and graphical structures and their application to expert systems. J. Royal Statistical Society B, 50:157-224, 1988.
7. V. K. Namasivayam, A. Pathak, and V. K. Prasanna. Scalable parallel implementation of Bayesian network to junction tree conversion for exact inference. In Proceedings of the 18th International Symposium on Computer Architecture and High Performance Computing, pages 167-176, 2006.
8. V. K. Namasivayam and V. K. Prasanna. Scalable parallel implementation of exact inference in bayesian networks. In Proceedings of the 12th International Conference on Parallel and Distributed Systems, pages 143-150, 2006.
9. D. Pennock. Logarithmic time parallel Bayesian inference. In Proceedings of the 14th Annual Conference on Uncertainty in Artificial Intelligence, pages 431-438, 1998.
10. S. J. Russell and P. Norvig. Artificial Intelligence: A Modern Approach (2nd Edition). Prentice Hall, December 2002.
11. E. Segal, B. Taskar, A. Gasch, N. Friedman, and D. Koller. Rich probabilistic models for gene expression. In 9th International Conference on Intelligent Systems for Molecular Biology, pages 243-252, 2001.
12. R. D. Shachter, S. K. Andersen, and P. Szolovits. Global conditioning for probabilistic inference in belief networks. In In Proceedings of the Tenth Conference on Uncertainty in Artifficial Intelligence, pages 514-522, 1994.
13. L. Yin, C.-H. Huang, and S. Rajasekaran. Parallel data mining of bayesian networks from gene expression data. In Poster Book of the 8th International Conference on Research in Computational Molecular Biology, pages 122-123, 2004.