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Conference Paper: On sampling from multivariate distributions

TitleOn sampling from multivariate distributions
Authors
KeywordsAlgorithm
Complexity
Sampling
Issue Date2011
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
Lecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2011, v. 6845 LNCS, p. 616-627 How to Cite?
AbstractLet X 1, X 2,..., X n be a set of random variables. Suppose that in addition to the prior distributions of these random variables we are also given linear constraints relating them. We ask for necessary and sufficient conditions under which we can efficiently sample the constrained distributions, find constrained marginal distributions for each of the random variables, etc. We give a tight characterization of the conditions under which this is possible. The problem is motivated by a number of scenarios where we have separate probabilistic inferences in some domain, but domain knowledge allows us to relate these inferences. When the joint prior distribution is a product distribution, the linear constraints have to be carefully chosen and are crucial in creating the lower bound instances. No such constraints are necessary if arbitrary priors are allowed. © 2011 Springer-Verlag.
Persistent Identifierhttp://hdl.handle.net/10722/188493
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252
References

 

DC FieldValueLanguage
dc.contributor.authorHuang, Zen_US
dc.contributor.authorKannan, Sen_US
dc.date.accessioned2013-09-03T04:08:43Z-
dc.date.available2013-09-03T04:08:43Z-
dc.date.issued2011en_US
dc.identifier.citationLecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics), 2011, v. 6845 LNCS, p. 616-627en_US
dc.identifier.issn0302-9743en_US
dc.identifier.urihttp://hdl.handle.net/10722/188493-
dc.description.abstractLet X 1, X 2,..., X n be a set of random variables. Suppose that in addition to the prior distributions of these random variables we are also given linear constraints relating them. We ask for necessary and sufficient conditions under which we can efficiently sample the constrained distributions, find constrained marginal distributions for each of the random variables, etc. We give a tight characterization of the conditions under which this is possible. The problem is motivated by a number of scenarios where we have separate probabilistic inferences in some domain, but domain knowledge allows us to relate these inferences. When the joint prior distribution is a product distribution, the linear constraints have to be carefully chosen and are crucial in creating the lower bound instances. No such constraints are necessary if arbitrary priors are allowed. © 2011 Springer-Verlag.en_US
dc.languageengen_US
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/en_US
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.subjectAlgorithmen_US
dc.subjectComplexityen_US
dc.subjectSamplingen_US
dc.titleOn sampling from multivariate distributionsen_US
dc.typeConference_Paperen_US
dc.identifier.emailHuang, Z: hzhiyi@cis.upenn.eduen_US
dc.identifier.authorityHuang, Z=rp01804en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1007/978-3-642-22935-0_52en_US
dc.identifier.scopuseid_2-s2.0-80052371755en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-80052371755&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume6845 LNCSen_US
dc.identifier.spage616en_US
dc.identifier.epage627en_US
dc.publisher.placeGermanyen_US
dc.identifier.scopusauthoridHuang, Z=55494568500en_US
dc.identifier.scopusauthoridKannan, S=7102340548en_US

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