Markovian combination of graphical model structures of undirected graphs

Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2009

Volumn , Issue , 2009, Pages 214-219

  Kim, Sung Ho ^a  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

Author keywords

Combined model structure; Graph separateness; Interaction graph; Markovian subgraph; Prime separator; Self connected separator

Indexed keywords

COMBINED MODEL; COMBINED MODEL STRUCTURE; GRAPH-SEPARATENESS; INTERACTION GRAPHS; MARKOVIAN; SUBGRAPHS;

ARTIFICIAL INTELLIGENCE; EARTHQUAKE RESISTANCE; GRAPHIC METHODS; SEPARATION; SEPARATORS; SPEECH RECOGNITION;

MODEL STRUCTURES;

EID: 74549174602     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. Chickering, D. (1996). Learning Bayesian networks is NP-complete, in D. Fisher and H. Lenz (Eds.) Learning from Data Springer-Verlag, 121-130.
2. Cooper, G.F. (1999). An overview of the representation and discovery of causal relationships using Bayesian networks, in C. Glymour and G.F. Cooper (Eds.) Computation, Causation, and Discovery, The MIT Press, 3-62.
3. Cox, D.R. and Wermuth, N. (1999). Likelihood factorizations for mixed discrete and continuous variables, Scand. J. Statist., 26, 209-220.
4. Fienberg, S.E. and Kim, S.-H. (1999). Combining conditional log-linear structures, J. Am. Statist. Ass., 445(94), 229-239.
5. Kim, S.-H. (2004). Combining decomposable model-structures, Research Report 04-15, Division of Applied Mathematics, KAIST, Daejeon, 305-701, S. Korea.
6. Kim, S.-H. (2006a). Conditional log-linear structures for log-linear modelling, Computational Statistics and Data Analysis, 50(8), 2044-2064.
7. Kim, S.-H. (2006b). Properties of Markovian subgraphs of a decomposable graph, Lecture Notes in Artificial Intelligence, LNAI 4293, 15-26.
8. Kim, S.-H. and Lee, S. (2008). Searching model structures based on marginal model structures. In New Developments in Robotics, Automation and Control (eds, A. Lazinica), Vienna, Austria: In-Tech Education and Publishing.
9. Lauritzen, S.L. (1996). Graphical Models. Oxford: Oxford University Press.
10. Lauritzen, S.L. and Spiegelhalter, D.J. (1988). Local computations with probabilities on graphical structures and their application to expert systems, J. R. Statist. Soc. B, 50(2), 157-224.
11. Pearl, J. (1986). Fusion, propagation and structuring in belief networks, Artificial Intelligence, 29, 241-288.
12. Pearl, J. (1988). Probabilistic Reasoning In Intelligent Systems: Networks of Plausible Inference, San Mateo, CA.: Morgan Kaufmann.
13. Pearl, J. and Paz, A. (1987). Graphoids: a graph based logic for reasoning about relevancy relations. In Advances in Artificial Intelligence II, (ed. B.D. Boulay, D. Hogg, and L. Steel), Amsterdam: North-Holland, pp. 357-63.
14. Whittaker, J. (1990). Graphical Models in Applied Multivariate Statistics, New York: Wiley