2016 PGM PGM 2016

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

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.

🚀 Conference Pioneer — PGM 2016
🧭 Keyword Pioneer — active part
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Reinforcement Learning