Context-Specific Likelihood Weighting

Nitesh Kumar, Ondřej Kuželka
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:2125-2133, 2021.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v130-kumar21b, title = { Context-Specific Likelihood Weighting }, author = {Kumar, Nitesh and Ku\v{z}elka, Ond\v{r}ej}, booktitle = {Proceedings of The 24th International Conference on Artificial Intelligence and Statistics}, pages = {2125--2133}, year = {2021}, editor = {Banerjee, Arindam and Fukumizu, Kenji}, volume = {130}, series = {Proceedings of Machine Learning Research}, month = {13--15 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v130/kumar21b/kumar21b.pdf}, url = {https://proceedings.mlr.press/v130/kumar21b.html}, 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. } }
Endnote
%0 Conference Paper %T Context-Specific Likelihood Weighting %A Nitesh Kumar %A Ondřej Kuželka %B Proceedings of The 24th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2021 %E Arindam Banerjee %E Kenji Fukumizu %F pmlr-v130-kumar21b %I PMLR %P 2125--2133 %U https://proceedings.mlr.press/v130/kumar21b.html %V 130 %X 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.
APA
Kumar, N. & Kuželka, O.. (2021). Context-Specific Likelihood Weighting . Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 130:2125-2133 Available from https://proceedings.mlr.press/v130/kumar21b.html.

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