Context-Specific Likelihood Weighting
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00351089" target="_blank" >RIV/68407700:21230/21:00351089 - isvavai.cz</a>
Result on the web
<a href="http://proceedings.mlr.press/v130/kumar21b.html" target="_blank" >http://proceedings.mlr.press/v130/kumar21b.html</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Context-Specific Likelihood Weighting
Original language description
Sampling is a popular method for approximate inference when exact inference is impractical. Generally, sampling algorithms do not exploit contextspecific 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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of Machine Learning Research
ISBN
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ISSN
2640-3498
e-ISSN
2640-3498
Number of pages
10
Pages from-to
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Publisher name
Proceedings of Machine Learning Research
Place of publication
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Event location
Virtual konference
Event date
Apr 13, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000659893802056