Relevant Path Separation: A Faster Method for Testing Independencies in Bayesian Networks

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Cory J. Butz, André E. dos Santos, Jhonatan S. Oliveira ;
Proceedings of the Eighth International Conference on Probabilistic Graphical Models, PMLR 52:74-85, 2016.

Abstract

\emphDirected separation (d-separation) played a fundamental role in the founding of \emphBayesian networks (BNs) and continues to be useful today in a wide range of applications. Given an independence to be tested, current implementations of d-separation explore the \emphactive part of a BN. On the other hand, an overlooked property of d-separation implies that d-separation need only consider the \emphrelevant part of a BN. We propose a new method for testing independencies in BNs, called \emphrelevant path separation (rp-separation), which explores the intersection between the active and relevant parts of a BN. Favourable experimental results are reported.

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