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Exponential Concentration for Mutual Information Estimation with Application to Forests

Author(s): Liu, Han; Lafferty, John; Wasserman, Larry

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dc.contributor.authorLiu, Han-
dc.contributor.authorLafferty, John-
dc.contributor.authorWasserman, Larry-
dc.date.accessioned2020-04-06T16:30:54Z-
dc.date.available2020-04-06T16:30:54Z-
dc.date.issued2012-12-01en_US
dc.identifier.citationLiu, H, Lafferty, J, Wasserman, L. (2012). Exponential concentration for mutual information estimation with application to forests. Advances in Neural Information Processing Systems, 4 (2537 - 2545). Retrieved from http://papers.nips.cc/paper/4768-exponential-concentration-for-mutual-information-estimation-with-application-to-forests.pdfen_US
dc.identifier.issn1049-5258-
dc.identifier.urihttp://papers.nips.cc/paper/4768-exponential-concentration-for-mutual-information-estimation-with-application-to-forests.pdf-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr17219-
dc.description.abstractWe prove a new exponential concentration inequality for a plug-in estimator of the Shannon mutual information. Previous results on mutual information estimation only bounded expected error. The advantage of having the exponential inequality is that, combined with the union bound, we can guarantee accurate estimators of the mutual information for many pairs of random variables simultaneously. As an application, we show how to use such a result to optimally estimate the density function and graph of a distribution which is Markov to a forest graph.en_US
dc.format.extent2537 - 2545en_US
dc.language.isoen_USen_US
dc.relation.ispartofAdvances in Neural Information Processing Systemsen_US
dc.rightsFinal published version. Article is made available in OAR by the publisher's permission or policy.en_US
dc.titleExponential Concentration for Mutual Information Estimation with Application to Forestsen_US
dc.typeJournal Articleen_US
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/conference-proceedingen_US

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