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. Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021. , vol.130 Proceedings of Machine Learning Research (PMLR), 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 (Poster) |
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Date Type: | Published Online |
Status: | Published |
Schools: | Computer Science & Informatics |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Publisher: | Proceedings of Machine Learning Research (PMLR) |
Date of First Compliant Deposit: | 21 October 2024 |
Last Modified: | 25 Oct 2024 14:30 |
URI: | https://orca.cardiff.ac.uk/id/eprint/173176 |
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