A non-parametric Bayesian method for inferring hidden causes

Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006

Volumn , Issue , 2006, Pages 536-543

  Wood, Frank ^a   Griffiths, Thomas L ^b   Ghahramani, Zoubin ^c  

^a Brown Univerity   (United States)

^b BROWN UNIVERSITY   (United States)

^c UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

FINITE NUMBER; GIBBS SAMPLERS; MEDICAL DATASET; NON-PARAMETRIC BAYESIAN; NONPARAMETRIC APPROACHES; REVERSIBLE JUMP MARKOV CHAIN MONTE CARLO; SIMULATED DATA; STRUCTURE-LEARNING;

ARTIFICIAL INTELLIGENCE; DATA PROCESSING; MARKOV PROCESSES;

BAYESIAN NETWORKS;

EID: 80053160972     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. N. Friedman and D. Koller, "Being Bayesian about network structure," UAI 16, 2000.
2. D. Heckerman, "A tutorial on learning with Bayesian networks," in Learning in Graphical Models, M. I. Jordan (ed.). MIT Press, 1998.
3. M. Shwe, B. Middleton, D. Heckerman, M. Henrion, E. Horvitz, H. Lehmann, and G. Cooper, "Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base I. the probabilistic model and inference algorithms," Methods of Information in Medicine, vol. 30, pp. 241-255, 1991.
4. G. Elidan and N. Friedman, "The information bottleneck expectation maximization algorithm," UAI 19, 2003.
5. P. Green, "Reversible jump Markov chain Monte Carlo computation and Bayesian model determination," Biometrika, pp. 711-732, 1995.
6. A. C. Courville, N. D. Daw, and D. S. Touretzky, "Similarity and discrimination in classical conditioning: A latent variable account," NIPS 17, 2005.
7. G. Orban, J. Fiser, R. N. Aslin, and M. Lengyel, "Bayesian model learning in human visual perception," NIPS 18, 2006.
8. C. Antoniak, "Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems," The Annals of Statistics, vol. 2, pp. 1152-1174, 1974.
9. R. M. Neal, "Markov chain sampling methods for Dirichlet process mixture models," Journal of Computational and Graphical Statistics, vol. 9, pp. 249-265, 2000.
10. C. Rasmussen, "The infinite Gaussian mixture model," NIPS 12, 2000.
11. T. L. Griffiths and Z. Ghahramani, "Infinite latent feature models and the Indian buffet process," Gatsby Unit, Tech. Rep. 2005-001, 2005.
12. J. Pearl, Probabilistic reasoning in intelligent systems, Morgan Kauffman, San Francisco, 1988.
13. S. Tuhrim, J. Reggia, and S. Goodall, "An experimental study of criteria for hypothesis plausibility," Journal of Experimental and Theoretical Artificial Intelligence, vol. 3, pp. 129-144, 1991.