Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move

Machine Learning

Volumn 71, Issue 2-3, 2008, Pages 265-305

  Grzegorczyk, Marco ^a,b   Husmeier, Dirk ^a,b  

^a Centre for Systems Biology at Edinburgh CSBE   (United Kingdom)

^b BIOMATHEMATICS AND STATISTICS SCOTLAND   (United Kingdom)

Author keywords

Bayesian networks; MCMC sampling; Structure learning

Indexed keywords

CONVERGENCE OF NUMERICAL METHODS; MATHEMATICAL MODELS; PARAMETER ESTIMATION; PROBABILITY DISTRIBUTIONS;

MCMC SAMPLING; STRUCTURE LEARNING;

BAYESIAN NETWORKS;

EID: 43049097125     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-008-5057-7     Document Type: Article

References (33)

1. Beinlich, I., Suermondt, R., Chavez, R., & Cooper, G. (1989). The alarm monitoring system: A case study with two probabilistic inference techniques for belief networks. In J. Hunter (Ed.), Proceedings of the second European conference on artificial intelligence and medicine. Berlin: Springer.
2. Castelo, R., & Kočka, T. (2003). On inclusion-driven learning of Bayesian networks. Journal of Machine Learning Research, 4, 527-574.
3. Chickering, D. M. (1995). A transformational characterization of equivalent Bayesian network structures. In International conference on uncertainty in artificial intelligence (UAI) (Vol. 11, pp. 87-98).
4. Chickering, D. M. (2002). Learning equivalence classes of Bayesian network structures. Journal of Machine Learning Research, 2, 445-498.
5. Cooper, G. F., & Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9, 309-347.
6. Eaton, D., & Murphy, K. (2007). Bayesian structure learning using dynamic programming and MCMC. In Proceedings of the twenty-third conference on uncertainty in artificial intelligence (UAI 2007).
7. Ellis, B., & Wong, W. (2006). Sampling Bayesian networks quickly. In Interface, Pasadena, CA.
8. Friedman, N., & Koller, D. (2003). Being Bayesian about network structure. Machine Learning, 50, 95-126.
9. Friedman, N., Linial, M., Nachman, I., & Pe'er, D. (2000). Using Bayesian networks to analyze expression data. Journal of Computational Biology, 7, 601-620.
10. Geiger, D., & Heckerman, D. (1994). Learning Gaussian networks. In Proceedings of the tenth conference on uncertainty in artificial intelligence (pp. 235-243).
11. Giudici, P., & Castelo, R. (2003). Improving Markov chain Monte Carlo model search for data mining. Machine Learning, 50, 127-158.
12. Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57, 97-109.
13. Husmeier, D. (2003). Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks. Bioinformatics, 19, 2271-2282.
14. Imoto, S., Higuchi, T., Goto, T., Kuhara, S., & Miyano, S. (2003). Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks. In Proceedings IEEE computer society bioinformatics conference (CSB'03) (pp. 104-113).
15. Imoto, S., Higuchi, T., Goto, T., & Miyano, S. (2006). Error tolerant model for incorporating biological knowledge with expression data in estimating gene networks. Statistical Methodology, 3(1), 1-16.
16. Jensen, F. V. (1996). An introduction to Bayesian networks. London: UCL Press.
17. Kanehisa, M. (1997). A database for post-genome analysis. Trends in Genetics, 13, 375-376.
18. Kanehisa, M., & Goto, S. (2000). Kegg: Kyoto encyclopedia of genes and genomes. Nucleic Acids Research, 28, 27-30.
19. Kanehisa, M., Goto, S., Hattori, M., Aoki-Kinoshita, K., Itoh, M., Kawashima, S., Katayama, T., Araki, M., & Hirakawa, M. (2006). From genomics to chemical genomics new developments in kegg. Nucleic Acids Research, 34, 354-357.
20. Kovisto, M. (2006). Advances in exact Bayesian structure discovery in Bayesian networks. In Proceedings of the twenty-second conference on uncertainty in artificial intelligence (UAI 2006).
21. Kovisto, M., & Sood, K. (2004). Exact Bayesian structure discovery in Bayesian networks. Journal of Machine Learning Research, 5, 549-573.
22. Larget, B., & Simon, D. L. (1999). Markov chain Monte Carlo algorithms for the Bayesian analysis of phylogenetic trees. Molecular Biology and Evolution, 16(6), 750-759.
23. Madigan, D., & York, J. (1995). Bayesian graphical models for discrete data. International Statistical Review, 63, 215-232.
24. Mansinghka, V. K., Kemp, C., Tenenbaum, J. B., & Griffiths, T. L. (2006). Structured priors for structure learning. In Proceedings of the twenty-second conference on uncertainty in artificial intelligence (UAI 2006).
25. Moore, A., & Wong, W. K. (2003). Optimal Reinsertion: a new search operator for accelerated and more accurate Bayesian network structure learning. In T. Fawcett & N. Mishra (Eds.), Proceedings of the 20th international conference on machine learning (ICML '03) (pp. 552-559). Menlo Park: AAAI Press.
26. Nariai, N., Tamada, Y., Imoto, S., & Miyano, S. (2005). Estimating gene regulatory networks and protein-protein interactions of saccharomyces cerevisiae from multiple genome-wide data. Bioinformatics, 21(Suppl. 2), ii206-ii212.
27. Newman, D. J., Hettich, S., Blake, C. L., & Merz, C. J. (1998). UCI repository of machine learning databases. http://www.ics.uci.edu/~mlearn/ MLRepository.html.
28. Ott, S., Imoto, S., & Miyano, S. (2004). Finding optimal models for small gene networks. In Pacific symposium on biocomputing (Vol. 9, pp. 557-567).
29. Pearl, J. (2000). Causality: models, reasoning and intelligent systems. London: Cambridge University Press.
30. Sachs, K., Perez, O., Pe'er, D. A., Lauffenburger, D. A., & Nolan, G. P. (2005). Protein-signaling networks derived from multiparameter single-cell data. Science, 308, 523-529.
31. Tierney, L. (1994). Markov chains for exploring posterior distributions. The Annals of Statistics, 22(4), 1701-1728.
32. Verma, T., & Pearl, J. (1990). Equivalence and synthesis of causal models. In Proceedings of the sixth conference on uncertainty in artificial intelligence (Vol. 6, pp. 220-227).
33. Werhli, A. V., & Husmeier, D. (2007). Reconstructing gene regulatory networks with Bayesian networks by combining expression data with multiple sources of prior knowledge. Statistical Applications in Genetics and Molecular Biology, 6 (Article 15).