Bayesian network learning with parameter constraints

Journal of Machine Learning Research

Volumn 7, Issue , 2006, Pages 1357-1383

  Niculescu, Radu Stefan ^a   Mitchell, Tom M ^b   Rao, R Bharat ^a  

^a Siemens Medical Solutions ^*  (United States)

^b Carnegie Mellon University   (United States)

Author keywords

Bayesian networks; Constrained optimization; Domain knowledge

Indexed keywords

CONSTRAINT THEORY; IMAGE ANALYSIS; OPTIMIZATION; PARAMETER ESTIMATION; PROBLEM SOLVING;

BAYESIAN NETWORKS; CONSTRAINED OPTIMIZATION; DOMAIN KNOWLEDGE; PARAMETER CONSTRAINTS;

LEARNING SYSTEMS;

EID: 33745794294     PISSN: 15337928     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (26)

1. J. Bilmes. Dynamic bayesian multinets. In Proceedings of UAI, pages 38-45, 2000.
2. C. Boutilier, N. Friedman, M. Goldszmidt, and D. Koller. Context-specific independence in bayesian networks. In. Proceedings of 12th UAI, pages 115-123, 1996.
3. P. A. Carpenter, M. A. Just, T. A. Keller, W. F. Eddy, and K. R. Thulborn. Time course of fMRI-activation in language and spatial networks during sentence comprehension. NeuroImage, 10:216-224, 1999.
4. A. M. Dale. Optimal experimental design for event-related fMRI. Human Brain Mapping, 8:109-114, 1999.
5. N. Friedman, L. Getoor, D. Koller, and A. Pfeffer. Learning probabilistic relational models. In Proceedings of 16th IJCAI, pages 1300-1307, 1999.
6. D. Geiger and D. Heckerman. Knowledge representation and inference in similarity networks and bayesian multinets. Artificial Intelligence, 82:45-74, 1996.
7. D. Geiger and D. Heckerman. A characterization of the dirichlet distribution through global and local parameter independence. The Annals of Statistics, 25:1344-1369, 1997.
8. D. Heckerman. A tutorial on learning with bayesian networks. In M. Jordan, editor, Learning in Graphical Models. MIT Press, Cambridge, MA, 1999.
9. P. Hooper. Dependent dirichlet priors and optimal linear estimators for belief net parameters. In AUAI Press, editor, Proceedings of the 20th Annual Conference on Uncertainty in Artificial Intelligence (UAI-04), pages 251-259, 2004.
10. R. Hutchinson, T.M. Mitchell, and I. Rustandi. Learning to identify overlapping and hidden cognitive processes from fMRI data. In. 11th Conference on Human Brain Mapping, June 2005.
11. R. Hutchinson, T.M. Mitchell, and I. Rustandi. Hidden process models. Technical Report CS-CALD-05-116, Carnegie Mellon University, February 2006.
12. D. Koller and A. Pfeffer. Object oriented bayesian networks. In Proceedings of 13th UAI, pages 302-313, 1997.
13. H. W. Kühn and A. W. Tucker. Nonlinear programming. In Proceedings of the Second Berkeley Symposium on Mathematical Statistics and Probability, pages 481-492. University of California Press, 1951.
14. T. Minka. The dirichlet-tree distribution. This unpublished paper is available online at http://research.microsoft.com/~minka/papers/dirichlet/minka-dirtree.pdf, 1999.
15. T. Mitchell, R. Hutchinson, M. Just, S. Newman, R. S. Niculescu, F. Pereira, and X. Wang. Learning to decode cognitive states from brain images. Machine Learning, 57(1-2):145-175, 2004.
16. K. P. Murphy. Dynamic Bayesian Networks: Representation, Inference and Learning. PhD thesis, UC Berkeley, 2002.
17. R. S. Niculescu. Exploiting parameter domain knowledge for learning in bayesian networks. Technical Report CMU-TR-05-147, Carnegie Mellon University, 2005.
18. R. S. Niculescu, T. Mitchell, and R. B. Rao. Parameter related domain knowledge for learning in graphical models. In Proceedings of SIAM Data Mining conference, 2005.
19. J. M. Pena, J. A. Lozano, and P. Larranaga. Learning recursive bayesian multinets for data clustering by means of constructive induction. Machine Learning, 47(1):63-89, 2002.
20. W. H. Press, S. A. Teukolsky, and W. T. Vetterling. Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, 1993.
21. R. L. Rabiner. A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257-286, 1989.
22. R. B. Rao, S. Sandilya, R. S. Niculescu, C. Germond, and H. Rao. Clinical and financial out-comes analysis with existing hospital patient records. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 416-425, 2003.
23. E. Segal, D. Pe'er, A. Regev, D. Koller, and N. Friedman. Learning module networks. In Proceedings of 19th UAI, pages 525-534, 2003.
24. J. B. Tenenbaum and W. T. Freeman. Separating style and content with bilinear models. Neural Computation, 12(6):1247-1283, 2000.
25. G. Welch and G. Bishop. An introduction to the kalman filter. Technical Report TR 95-041, University of North Carolina, 1995.
26. C. Zhai and J. Lafferty. A study of smoothing methods for language models applied to ad hoc information retrieval. In Proceedings of SIGIR, pages 334-342, 2001.