101. A Tight Upper Bound on Mutual Information
- Author
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Michal Hledik, Gašper Tkačik, and Thomas R. Sokolowski
- Subjects
Decodes ,FOS: Computer and information sciences ,Computer science ,Computer Science - Information Theory ,Equivocation ,02 engineering and technology ,Data_CODINGANDINFORMATIONTHEORY ,Upper and lower bounds ,03 medical and health sciences ,Joint probability distribution ,0202 electrical engineering, electronic engineering, information engineering ,Maximum a posteriori estimation ,030304 developmental biology ,Computer Science::Information Theory ,Conditional entropy ,0303 health sciences ,biology ,Information Theory (cs.IT) ,020206 networking & telecommunications ,Mutual information ,16. Peace & justice ,biology.organism_classification ,FOS: Biological sciences ,Quantitative Biology - Neurons and Cognition ,Neurons and Cognition (q-bio.NC) ,Algorithm ,Communication channel - Abstract
We derive a tight lower bound on equivocation (conditional entropy), or equivalently a tight upper bound on mutual information between a signal variable and channel outputs. The bound is in terms of the joint distribution of the signals and maximum a posteriori decodes (most probable signals given channel output). As part of our derivation, we describe the key properties of the distribution of signals, channel outputs and decodes, that minimizes equivocation and maximizes mutual information. This work addresses a problem in data analysis, where mutual information between signals and decodes is sometimes used to lower bound the mutual information between signals and channel outputs. Our result provides a corresponding upper bound., 6 pages, 3 figures; proof illustration added
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