Parallel algorithm for learning optimal bayesian network structure

Journal of Machine Learning Research

Volumn 12, Issue , 2011, Pages 2437-2459

  Tamada, Yoshinori ^a   Imoto, Seiya ^a   Miyano, Satoru ^a,b  

^a UNIVERSITY OF TOKYO   (Japan)

^b RIKEN   (Japan)

Author keywords

Optimal Bayesian network structure; Parallel algorithm

Indexed keywords

BAYESIAN NETWORK STRUCTURE; COMPUTATIONAL EXPERIMENT; DISTRIBUTED MEMORY; DYNAMIC PROGRAMMING ALGORITHM; MEMORY REQUIREMENTS; NETWORK SEARCH; OPTIMAL STRUCTURES; PARALLELIZATION EFFICIENCY; PARALLELIZATIONS; SEARCH SPACES; SINGLE PROCESSORS; SPACE COMPLEXITY; SPACE OVERHEAD;

BAYESIAN NETWORKS; DISTRIBUTED PARAMETER NETWORKS; DYNAMIC PROGRAMMING; INFERENCE ENGINES; INTELLIGENT NETWORKS; PARALLEL ALGORITHMS; PARALLEL ARCHITECTURES; PARALLEL PROCESSING SYSTEMS; STRUCTURAL OPTIMIZATION; SUPERCOMPUTERS;

COMPUTATIONAL EFFICIENCY;

EID: 80052190545     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (13)

1. B. P. Buckles and M. Lybanon. Algorithm 515: Generation of a vector from the lexicographical index [G6]. ACM Transductions on Mathematical Software, 3(2): 180-182, 1977.
2. D. M. Chickering, D. Geiger, and D. Heckerman. Learning Bayesian networks: Search methods and experimental results. In Proceedings of the Fifth Conference on Artificial Intelligence and Statistics, pages 112-128, 1995.
3. N. Friedman, M. Linial, I. Nachman, and D. Pe'er. Using Bayesian networks to analyze expression data. J. Computational Biology, 7:601-620, 2000.
4. D. Heckerman, D. Geiger, and D. M. Chickering. Learning Bayesian networks: the combination of knowledge and statistical data. Machine Learning, 20:197-243, 1995.
5. S. Imoto, T. Goto, and S. Miyano. Estimation of genetic networks and functional structures between genes by using Bayesian networks and nonparametric regression. Pacific Symposium on Biocomputing, 7:175-186, 2002.
6. M. Koivisto and K. Sood. Exact Bayesian structure discovery in Bayesian networks. Journal of Machine Learning Research, 5:549-573, 2004.
7. S. Ott, S. Imoto, and S. Miyano. Finding optimal models for small gene networks. Pacific Symposium on Biocomputing, 9:557-567, 2004.
8. P. Parviainen and M. Koivisto. Exact structure discovery in Bayesian networks with less space. Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009), 2009.
9. J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufman Publishers, San Mateo, CA, 1988.
10. E. Perrier, S. Imoto, and S. Miyano. Finding optimal Bayesian network given a super-structure. J. Machine Learning Research, 9:2251-2286, 2008.
11. T. Silander and P. Myllymäki. A simple approach for finding the globally optimal Bayesian network structure. Proceedings of the 22th Conference on Uncertainty in Artificial Intelligence (UAI 2006), pages 445-452, 2006.
12. Y. Tamada, S. Imoto, and S. Miyano. Conversion between a combination vector and the lexicographical index in linear time with polynomial time preprocessing, 2011. submitted.
13. I. Tsamardinos, L. E. Brown, and C. F. Aliferis. The max-min hill-climbing Bayesian network structure learning algorithm. Machine Learning, 65:31-78, 2006. (Pubitemid 44451193)