NDLI: A tight lower bound on the mutual information of a binary and an arbitrary finite random variable as a function of the variational distance

Content Provider	IEEE Xplore Digital Library
Author	Stefani, A.G. Huber, J.B. Jardin, C. Sticht, H.
Copyright Year	2014
Description	Author affiliation: Inst. for Inf. Transm. (LIT) FAU Erlangen-Nuremberg Erlangen, Erlangen, Germany (Stefani, A.G.; Huber, J.B.) \|\| Bioinf., Inst. for Biochem., FAU Erlangen-Nuremberg, Erlangen, Germany (Jardin, C.; Sticht, H.)
Abstract	In this paper a numerical method is presented, which finds a tight lower bound for the mutual information between a binary and an arbitrary finite random variable with joint distributions that have variational distance to a known joint distribution not greater than a known value. This lower bound can be applied to mutual information estimation with confidence intervals.
Sponsorship	South Australian/Australian Capital Territory Joint Sect. IT Chapt.
Starting Page	1
Ending Page	4
File Size	215079
Page Count	4
File Format	PDF
ISBN	9781479933549
DOI	10.1109/AusCTW.2014.6766418
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-02-03
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Conferences Minimization Probability distribution Random variables Joints Mutual information Optimization
Content Type	Text
Resource Type	Article

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A tight lower bound on the mutual information of a binary and an arbitrary finite random variable as a function of the variational distance