DocumentCode :
23792
Title :
Which Boolean Functions Maximize Mutual Information on Noisy Inputs?
Author :
Courtade, Thomas A. ; Kumar, G. Ramesh
Author_Institution :
Univ. of California at Berkeley, Berkeley, CA, USA
Volume :
60
Issue :
8
fYear :
2014
fDate :
Aug. 2014
Firstpage :
4515
Lastpage :
4525
Abstract :
We pose a simply stated conjecture regarding the maximum mutual information a Boolean function can reveal about noisy inputs. Specifically, let Xn be independent identically distributed Bernoulli(1/2), and let Yn be the result of passing Xn through a memoryless binary symmetric channel with crossover probability α. For any Boolean function b : {0, 1}n → {0, 1}, we conjecture that I(b(Xn); Yn) ≤ 1 - H(α). While the conjecture remains open, we provide substantial evidence supporting its validity. Connections are also made to discrete isoperimetric inequalities.
Keywords :
Boolean functions; information theory; probability; Boolean functions; crossover probability; discrete isoperimetric inequalities; independent identically distributed Bernoulli; maximum mutual information; memoryless binary symmetric channel; noisy inputs; simply stated conjecture; Boolean functions; Context; Entropy; Mutual information; Noise measurement; Random variables; Tin; Boolean functions; extremal inequality; isoperimetric inequality; mutual information;
fLanguage :
English
Journal_Title :
Information Theory, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9448
Type :
jour
DOI :
10.1109/TIT.2014.2326877
Filename :
6822629
Link To Document :
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