Local Markov Property for Models Satisfying Composition Axiom
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.
Cite
Text
Kang and Tian. "Local Markov Property for Models Satisfying Composition Axiom." Conference on Uncertainty in Artificial Intelligence, 2005.Markdown
[Kang and Tian. "Local Markov Property for Models Satisfying Composition Axiom." Conference on Uncertainty in Artificial Intelligence, 2005.](https://mlanthology.org/uai/2005/kang2005uai-local/)BibTeX
@inproceedings{kang2005uai-local,
title = {{Local Markov Property for Models Satisfying Composition Axiom}},
author = {Kang, Changsung and Tian, Jin},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2005},
pages = {284-291},
url = {https://mlanthology.org/uai/2005/kang2005uai-local/}
}