Mixed cumulative distribution networks

Journal of Machine Learning Research

Volumn 15, Issue , 2011, Pages 670-678

  Silva, Ricardo ^a   Blundell, Charles ^b   Teh, Yee Whye ^b  

^a MAX DELBRÜCK CENTER FOR MOLECULAR MEDICINE   (Germany)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CUMULATIVE DISTRIBUTION; DIRECTED ACYCLIC GRAPHS; LATENT VARIABLE; MIXED GRAPH; PARAMETERIZATIONS;

ARTIFICIAL INTELLIGENCE; PARAMETER ESTIMATION; PROBABILITY DISTRIBUTIONS;

DIRECTED GRAPHS;

EID: 84862302578     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (28)

1. Bollen, K. (1989). Structural Equations with Latent Variables. John Wiley & Sons.
2. Drton, M. and Richardson, T. (2003). A new algorithm for maximum likelihood estimation in Gaussian models for marginal independence. Proceedings of the 19th Conference on Uncertainty in Artificial Intelligence.
3. Drton, M. and Richardson, T. (2008). Binary models for marginal independence. Journal of the Royal Statistical Society, Series B, 70:287-309.
4. Evans, R. and Richardson, T. (2010). Maximum likelihood fitting of acyclic directed mixed graphs to binary data. Proceedings of the 26th InternationalConference on Uncertainty in Artificial Intelligence.
5. Frank, A. and Asuncion, A. (2010). UCI machine learning repository.
6. Huang, J. (2009). Cumulative Distribution Networks: Inference, Estimation and Applications of GraphicalModels for Cumulative Distribution Functions. PhD Thesis, University of Toronto, Department of Computer Science.
7. Huang, J. and Frey, B. (2008). Cumulative distribution networks and the derivative-sum-product algorithm. Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence.
8. Huang, J. and Jojic, N. (2010). Maximum-likelihood learning of cumulative distribution functions on graphs. 13th International Conference on Artificial Intelligence and Statistics, AISTATS.
9. Huang, J., Jojic, N., and Meek, C. (2010). Exact inference and learning of cumulative distribution functions on loopy graphs. Neural Information Systems Processing.
10. Jacobs, R., Jordan, M., Nowlan, S., and Hinton, G. (1991). Adaptivemixtures of local experts. Neural Computation, 3:79-87.
11. Joe, H. (1997). Multivariate Models and Dependence Concepts. Chapman-Hall.
12. Khare, K. and Rajaratnam, B. (2009). Wishart distributions for covariance graph models. Technical Report. Department of Statistics, Stanford University.
13. Kirshner, S. (2007). Learning with tree-averaged densities and distributions. Neural Information Processing Systems.
14. Lauritzen, S. (1996). GraphicalModels. Oxford University Press.
15. Liebscher, E. (2008). Construction of asymmetric multivariate copulas. Journal of Multivariate Analysis, 99:2234-2250.
16. Nelsen, R. (2007). An Introduction to Copulas. Springer-Verlag.
17. Pearl, J. (1988). Probabilistic Reasoning in Expert Systems: Networks of Plausible Inference. Morgan Kaufmann.
18. Pellet, J.-P. (2008). Finding latent causes in causal networks: an efficient approach based on Markov blankets. Neural Information Processing Systems.
19. Richardson, T. (2003). Markov properties for acyclic directed mixed graphs. Scandinavian Journal of Statistics, 30:145-157.
20. Richardson, T. (2009). A factorization criterion for acyclic directed mixed graphs. Proceedings of the 25th International Conference on Uncertainty in Artificial Intelligence.
21. Richardson, T. and Spirtes, P. (2002). Ancestral graph Markov models. Annals of Statistics, 30:962-1030.
22. Rothman, A., Levina, E., and Zhu, J. (2009). Generalized thresholding of large covariancematrices. Journal of the American Statistical Association, 104:177-186.
23. Silva, R., Chu, W., and Ghahramani, Z. (2007). Hidden common cause relations in relational learning. Neural Information Processing Systems.
24. Silva, R. and Ghahramani, Z. (2009). The hidden life of latent variables: Bayesian learning with mixed graphmodels. Journal of Machine Learning Research, 10:1187-1238.
25. Spirtes, P., Glymour, C., and Scheines, R. (2000). Causation, Prediction and Search. Cambridge University Press.
26. Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, pages 557-585.
27. Zellner, A. (1962). An efficientmethod of estimating seemingly unrelated regression equations and tests for aggregation bias. Journal of the American Statistical Association, 57:348-368.
28. Zhang, J. (2008). Causal reasoning with ancestral graphs. Journal of Machine Learning Research, 9:1437-1474.