d-Separation: From Theorems to Algorithms

Preprint

27 March 2013

preprint
Published by arXiv in arXiv

https://doi.org/10.48550/arXiv.1304.1505

Abstract

An efficient algorithm is developed that identifies all independencies implied by the topology of a Bayesian network. Its correctness and maximality stems from the soundness and completeness of d-separation with respect to probability theory. The algorithm runs in time O (l E l) where E is the number of edges in the network.

Keywords

INDEPENDENCIES IMPLIED
EFFICIENT ALGORITHM
PROBABILITY THEORY
BAYESIAN NETWORK
MAXIMALITY STEMS
ALGORITHM RUNS
SEPARATION
TOPOLOGY

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Version 1, 2013-03-27, ArXiv

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