Bayesian networks

Communications of the ACM

Volumn 53, Issue 12, 2010, Pages 80-90

  Darwiche, Adnan ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DISTRIBUTED PARAMETER NETWORKS; INFERENCE ENGINES; INTELLIGENT NETWORKS;

BAYESIAN NETWORKS;

EID: 78650115042     PISSN: 00010782     EISSN: 15577317     Source Type: Journal    
DOI: 10.1145/1859204.1859227     Document Type: Review

References (36)

1. Bayes, T. An essay towards solving a problem in the doctrine of chances. Phil. Trans. 3 (1963), 370-418. Reproduced in W.E. Deming.
2. Blei, D.M., Ng, A.Y. and Jordan, M.I. Latent Dirichlet allocation. Journal of Machine Learning Research 3 (2003), 993-1022.
3. Boutilier, C., Friedman, N., Goldszmidt, M. and Koller, D. Context-specific independence in Bayesian networks. In Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence (1996), 115-123.
4. Chavira, M., Darwiche, A. and Jaeger, M. Compiling relational Bayesian networks for exact inference. International Journal of Approximate Reasoning 42, 1-2 (May 2006) 4-20.
5. Cowell, R., Dawid, A., Lauritzen, S. and Spiegelhalter, D. Probabilistic Networks and Expert Systems. Springer, 1999.
6. D arwiche, A. Recursive conditioning. Artificial Intelligence 126, 1-2 (2001), 5-41.
7. Darwiche, A. A differential approach to inference in Bayesian networks. Journal of the ACM 50, 3 (2003).
8. Darwiche, A. Modeling and Reasoning with Bayesian Networks. Cambridge University Press, 2009.
9. Dean, T. and Kanazawa, K. A model for reasoning about persistence and causation. Computational Intelligence 5, 3 (1989), 142-150.
10. D echter, R. Bucket elimination: A unifying framework for probabilistic inference. In Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence (1996), 211-219.
11. Edwards, D. Introduction to Graphical Modeling. Springer, 2nd edition, 2000.
12. Fishelson, M. and Geiger, D. Exact genetic linkage computations for general pedigrees. Bioinformatics 18, 1 (2002), 189-198.
13. Frey, B. editor. Graphical Models for Machine Learning and Digital Communication. MIT Press, Cambridge, MA, 1998.
14. Friedman, N., Geiger, D. and Goldszmidt, M. Bayesian network classifiers. Machine Learning 29, 2-3 (1997), 131-163.
15. Gilks, W., Richardson, S. and Spiegelhalter, D. Markov Chain Monte Carlo in Practice: Interdisciplinary Statistics. Chapman & Hall/CRC, 1995.
16. Glymour, C. and Cooper, G. eds. Computation, Causation, and Discovery. MIT Press, Cambridge, MA, 1999.
17. Heckerman, D. A tutorial on learning with Bayesian networks. Learning in Graphical Models. Kluwer, 1998, 301-354.
18. Howard, R.A. and Matheson, J.E. Influence diagrams. Principles and Applications of Decision Analysis, Vol. 2. Strategic Decision Group, Menlo Park, CA, 1984, 719-762.
19. J aakkola, T. Tutorial on variational approximation methods. Advanced Mean Field Methods. D. Saad and M. Opper, ed, MIT Press, Cambridge, MA , 2001, 129-160.
20. Jensen, F.V. and Nielsen, T.D. Bayesian Networks and Decision Graphs. Springer, 2007.
21. Jordan, M., Ghahramani, Z., Jaakkola, T. and Saul, L. An introduction to variational methods for graphical models. Machine Learning 37, 2 (1999), 183-233.
22. Koller, D. and Friedman, N. Probabilistic Graphical Models: Principles and Techniques. MIT Press, Cambridge, MA , 2009.
23. Koller, D. and Pfeffer, A. Object-oriented Bayesian networks. In Proceedings of the 13th Conference on Uncertainty in Artificial Intelligence (1997), 302-313.
24. Lauritzen, S.L. and Spiegelhalter, D.J. Local computations with probabilities on graphical structures and their application to expert systems. Journal of Royal Statistics Society, Series B 50, 2 (1998), 157-224.
25. Mengshoel, O., Darwiche, A., Cascio, K., Chavira, M., Poll, S. and Uckun, S. Diagnosing faults in electrical power systems of spacecraft and aircraft. In Proceedings of the 20th Innovative Applications of Artificial Intelligence Conference (2008), 1699-1705.
26. M ilch, B., Marthi, B., Russell, S., Sontag, D., Ong, D. and Kolobov, A. BLOG : Probabilistic models with unknown objects. In Proceedings of the International Joint Conference on Artificial Intelligence (2005), 1352-1359.
27. Neapolitan, R. Learning Bayesian Networks. Prentice Hall, Englewood, NJ , 2004.
28. Pearl, J. Bayesian networks: A model of selfactivated memory for evidential reasoning. In Proceedings of the Cognitive Science Society (1985), 329-334.
29. Pearl, J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988.
30. Pearl, J. Causality: Models, Reasoning, and Inference. Cambridge University Press, 2000.
31. Durbin, A.K.R., Eddy, S. and Mitchison, G. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, 1998.
32. Smyth, P., Heckerman, D. and Jordan, M. Probabilistic independence networks for hidden Markov probability models. Neural Computation 9, 2 (1997), 227-269.
33. Steyvers, M. and Griffiths, T. Probabilistic topic models. Handbook of Latent Semantic Analysis. T. K. Landauer, D. S. McNamara, S. Dennis, and W. Kintsch, eds. 2007, 427-448.
34. Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M.F. and Rother, C. A comparative study of energy minimization methods for Markov random fields with smoothness-based priors. IEEE Trans. Pattern Anal. Mach. Intell. 30, 6 (2008), 1068-1080.
35. Y edidia, J., Freeman, W. and Weiss, Y. Constructing free-energy approximations and generalized belief propagation algorithms. IEEE Transactions on Information Theory 1, 7 (2005), 2282-2312.
36. Zhang, N.L. and Poole, D. A simple approach to Bayesian network computations. In Proceedings of the 10th Conference on Uncertainty in Artificial Intelligence, (1994), 171-178.