Uncertainty in Artificial Intelligence
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Local Markov Property for Models Satisfying Composition Axiom
Changsung Kang, Jin Tian
Abstract:
The local Markov condition for a DAG to be an independence map of a probability distribution is well known. For DAGs with latent variables, represented as bi-directed edges in the graph, the local Markov property may invoke exponential number of conditional independencies. This paper shows that the number of conditional independence relations required may be reduced if the probability distributions satisfy the composition axiom. In certain types of graphs, only linear number of conditional independencies are required. The result has applications in testing linear structural equation models with correlated errors.
Keywords:
Pages: 284-291
PS Link:
PDF Link: /papers/05/p284-kang.pdf
BibTex:
@INPROCEEDINGS{Kang05,
AUTHOR = "Changsung Kang and Jin Tian",
TITLE = "Local Markov Property for Models Satisfying Composition Axiom",
BOOKTITLE = "Proceedings of the Twenty-First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-05)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2005",
PAGES = "284--291"
}


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