Maximum likelihood fitting of acyclic directed mixed graphs to binary data

Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010

Volumn , Issue , 2010, Pages 177-184

  Evans, Robin J ^a   Richardson, Thomas S ^a  

^a UNIVERSITY OF WASHINGTON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; DIRECTED GRAPHS; MARKOV PROCESSES;

BINARY DATA; CONDITIONAL INDEPENDENCES; LATENT VARIABLE; MIXED GRAPH; SEMI MARKOV MODEL;

MAXIMUM LIKELIHOOD ESTIMATION;

EID: 80053152166     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Ali, A., T. S. Richardson, P. Spirtes, and J. Zhang (2005). Towards characterizing Markov equivalence classes for directed acyclic graphs with latent variables. In UAI-05, pp. 10-17.
2. Bertsekas, D. P. (1999). Nonlinear Programming (Second ed.). Athena Scientific.
3. Dawid, A. P. (1979). Conditional independence in statistical theory (with discussion). J. Roy. Statist. Soc. Ser. B 41, 1-31.
4. Drton, M. (2009). Likelihood ratio tests and singularities. Ann. Statist. 37, 979-1012.
5. Drton, M. and T. S. Richardson (2008a). Binary models for marginal independence. J. Roy. Statist. Soc. Ser. B 70 (2), 287-309.
6. Drton, M. and T. S. Richardson (2008b). Graphical methods for efficient likelihood inference in Gaussian covariance models. JMLR 9, 893-914.
7. Fienberg, S. E. (1980). The Analysis of Cross- Classified Categorical Data (Second ed.). Cambridge, Mass.: MIT Press.
8. Huang, J. and B. Frey (2008). Cumulative distribution networks and the derivative-sum-product algorithm. In UAI-08.
9. Huang, Y. and M. Valtorta (2006). Pearl's calculus of intervention is complete. In UAI-06.
10. Johansen, S. (1979). Introduction to the theory of regular exponential families. Institute of Mathematical Statistics, University of Copenhagen, Copenhagen.
11. Lang, J., J. McDonald, and P. Smith (1999). Association-marginal modeling of multivariate categorical responses: A maximum likelihood approach. J. Amer. Statist. Assoc. 94 (448), 1161-71.
12. Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann.
13. R Development Core Team (2004). R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. www.Rproject.org.
14. Richardson, T. S. (2003). Markov properties for acyclic directed mixed graphs. Scand. J. Statist. 30 (1), 145-157.
15. Richardson, T. S. (2009). A factorization criterion for acyclic directed mixed graphs. In UAI-09.
16. Richardson, T. S. and P. Spirtes (2002). Ancestral graph Markov models. Ann. Statist. 30, 962-1030. (Pubitemid 37095336)
17. Richardson, T. S. and P. Spirtes (2003). Causal inference via ancestral graph models (with discussion). In P. Green, N. Hjort, and S. Richardson (Eds.), Highly Structured Stochastic Systems, Chapter 3, pp. 83-105. Oxford, UK: Oxford University Press.
18. Shpitser, I. and J. Pearl (2006). Identification of conditional interventional distributions. In UAI-06.
19. Silva, R. and Z. Ghahramani (2009). The hidden life of latent variables: Bayesian learning with mixed graph models. JMLR 10, 1187-1238.