Context-specific likelihood weighting

Kumar, Nitesh ORCID: https://orcid.org/0000-0002-9301-3876 and Kuželka, Ondřej 2021. Context-specific likelihood weighting. Presented at: The 24th International Conference on Artificial Intelligence and Statistics, Virtual, 13 -15 April 2021. Published in: Banerjee, Arindam and Fukumizu, Kenji eds. Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021. Proceedings of Machine Learning Research. , vol.130 ML Research Press, pp. 2125-2133.

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Abstract

Sampling is a popular method for approximate inference when exact inference is impractical. Generally, sampling algorithms do not exploit context-specific independence (CSI) properties of probability distributions. We introduce context-specific likelihood weighting (CS-LW), a new sampling methodology, which besides exploiting the classical conditional independence properties, also exploits CSI properties. Unlike the standard likelihood weighting, CS-LW is based on partial assignments of random variables and requires fewer samples for convergence due to the sampling variance reduction. Furthermore, the speed of generating samples increases. Our novel notion of contextual assignments theoretically justifies CS-LW. We empirically show that CS-LW is competitive with state-of-the-art algorithms for approximate inference in the presence of a significant amount of CSIs.

Item Type:	Conference or Workshop Item - published (Poster)
Date Type:	Published Online
Status:	Published
Schools:	Schools > Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	ML Research Press
ISSN:	2640-3498
Date of First Compliant Deposit:	21 October 2024
Last Modified:	16 Jul 2026 09:36
URI:	https://orca.cardiff.ac.uk/id/eprint/173176

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