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Error exponents for composite hypothesis testing of Markov forest distributions

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dc.contributor.author Tan, Vincent Yan Fu
dc.contributor.author Anandkumar, Animashree
dc.contributor.author Willsky, Alan S.
dc.date.accessioned 2012-10-03T19:16:37Z
dc.date.available 2012-10-03T19:16:37Z
dc.date.issued 2010-07
dc.date.submitted 2010-06
dc.identifier.isbn 978-1-4244-7891-0
dc.identifier.isbn 978-1-4244-7890-3
dc.identifier.uri http://hdl.handle.net/1721.1/73578
dc.description.abstract The problem of composite binary hypothesis testing of Markov forest (or tree) distributions is considered. The worst-case type-II error exponent is derived under the Neyman-Pearson formulation. Under simple null hypothesis, the error exponent is derived in closed-form and is characterized in terms of the so-called bottleneck edge of the forest distribution. The least favorable distribution for detection is shown to be Markov on the second-best max-weight spanning tree with mutual information edge weights. A necessary and sufficient condition to have positive error exponent is derived. en_US
dc.language.iso en_US
dc.publisher Institute of Electrical and Electronics Engineers (IEEE) en_US
dc.relation.isversionof http://dx.doi.org/10.1109/ISIT.2010.5513399 en_US
dc.rights Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. en_US
dc.source IEEE en_US
dc.title Error exponents for composite hypothesis testing of Markov forest distributions en_US
dc.type Article en_US
dc.identifier.citation Tan, Vincent Y. F., Animashree Anandkumar, and Alan S. Willsky. “Error Exponents for Composite Hypothesis Testing of Markov Forest Distributions.” IEEE International Symposium on Information Theory Proceedings (ISIT), 2010. 1613–1617. © Copyright 2010 IEEE en_US
dc.contributor.department Massachusetts Institute of Technology. Laboratory for Information and Decision Systems en_US
dc.contributor.department Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science en_US
dc.contributor.mitauthor Tan, Vincent Yan Fu
dc.contributor.mitauthor Anandkumar, Animashree
dc.contributor.mitauthor Willsky, Alan S.
dc.relation.journal Proceedings of the IEEE International Symposium on Information Theory Proceedings (ISIT), 2010 en_US
dc.identifier.mitlicense PUBLISHER_POLICY en_US
dc.eprint.version Final published version en_US
dc.type.uri http://purl.org/eprint/type/ConferencePaper en_US
dspace.orderedauthors Tan, Vincent Y. F.; Anandkumar, Animashree; Willsky, Alan S. en


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