Relevant Path Separation: A Faster Method for Testing Independencies in Bayesian Networks
Cory J. Butz, André Evaristo dos Santos, Jhonatan S. Oliveira · Probabilistic Graphical Models · 2016
Directed separation (d-separation) played a fundamental role in the founding of Bayesian 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 active part of a BN. On the other hand, an overlooked property of d-separation implies that d-separation need only consider the relevant part of a BN. We propose a new method for testing independencies in BNs, called relevant path separation (rp-separation), which explores the intersection between the active and relevant parts of a BN. Favourable experimental results are reported.