Unsupervised learning of noisy-or Bayesian networks

Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, UAI 2013

Volumn , Issue , 2013, Pages 272-281

  Halpern, Yoni ^a   Sontag, David ^a  

^a POLYTECHNIC UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DISCRETE VARIABLES; EXPECTATION - MAXIMIZATIONS; GROUND TRUTH; HIDDEN VARIABLE; MIXTURE MODEL; POLYNOMIAL-TIME ALGORITHMS; TRAINING DATA; VARIATIONAL METHODS;

ARTIFICIAL INTELLIGENCE; DIAGNOSIS; INFERENCE ENGINES; LEARNING ALGORITHMS; MONTE CARLO METHODS;

BAYESIAN NETWORKS;

EID: 84888163727     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Allman, Elizabeth S, Matias, Catherine, & Rhodes, John A. 2009. Identifiability of parameters in latent structure models with many observed variables. The Annals of Statistics, 37(6A), 3099-3132.
2. Anandkumar, A., Hsu, D., & Kakade, S. 2012a. A method of moments for mixture models and hidden Markov models. In: COLT.
3. Anandkumar, Anima, Foster, Dean, Hsu, Daniel, Kakade, Sham, & Liu, Yi-Kai. 2012b. A spectral algorithm for latent Dirichlet allocation. Pages 926-934 of: Advances in Neural Information Processing Systems 25.
4. Anandkumar, Animashree, Hsu, Daniel, Javanmard, Adel, & Kakade, Sham M. 2012c. Learning Linear Bayesian Networks with Latent Variables. arXiv preprint arXiv: 1209.5350.
5. Anandkumar, Animashree, Hsu, Daniel, & Kakade, Sham M. 2012d. A method of moments for mixture models and hidden Markov models. arXiv preprint arXiv: 1203.0683.
6. Berge, Jos M.F. 1991. Kruskal's polynomial for 2×2×2 arrays and a generalization to 2 × n × n arrays. Psychometrika, 56, 631-636.
7. Chang, Joseph T. 1996. Full reconstruction of Markov models on evolutionary trees: identifiability and consistency. Mathematical biosciences, 137(1), 51-73.
8. Cooper, Gregory F. 1987. Probabilistic Inference Using Belief Networks Is NP-Hard. Technical Report BMIR-1987-0195. Medical Computer Science Group, Stanford University.
9. Heckerman, David E. 1990. A tractable inference algorithm for diagnosing multiple diseases. Knowledge Systems Laboratory, Stanford University.
10. Hsu, D., and Kakade., S. M., & Liang, P. 2012. Identifiability and Unmixing of Latent Parse Trees. In: Advances in Neural Information Processing Systems (NIPS).
11. Jaakkola, Tommi S, & Jordan, Michael I. 1999. Variational Probabilistic Inference and the QMR-DT Network. Journal of Artificial Intelligence Research, 10, 291-322.
12. Kearns, Michael, & Mansour, Yishay. 1998. Exact inference of hidden structure from sample data in noisy-OR networks. Pages 304-310 of: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc.
13. Miller, Randolph A., Pople, Harry E., & Myers, Jack D. 1982. Internist-I, an Experimental Computer-Based Diagnostic Consultant for General Internal Medicine. New England Journal of Medicine, 307(8), 468-476.
14. Miller, Randolph A., McNeil, Melissa A., Challinor, Sue M., Fred E. Masarie, Jr., & Myers, Jack D. 1986. The INTERNIST-1/QUICK MEDICAL REFERENCE project - Status report. West J Med, 145(Dec), 816-822.
15. Morris, Quaid. 2001. Anonymised QMR KB to aQMR-DT.
16. Mossel, Elchanan, & Roch, Sébastien. 2005. Learning nonsingular phylogenies and hidden Markov models. Pages 366-375 of: Proceedings of the thirty-seventh annual ACM symposium on Theory of computing. ACM.
17. Ng, Andrew Y, & Jordan, Michael I. 2000. Approximate inference algorithms for two-layer Bayesian networks. Advances in neural information processing systems, 12.
18. Shwe, Michael A, Middleton, B, Heckerman, DE, Henrion, M, Horvitz, EJ, Lehmann, HP, & Cooper, GF. 1991. Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base. Meth. Inform. Med, 30, 241-255.
19. Šingliar, Tomáš, & Hauskrecht, Miloš. 2006. Noisy-or component analysis and its application to link analysis. The Journal of Machine Learning Research, 7, 2189-2213.