Worst-case Compressibility of Discrete and Finite Distributions

Details Worst-case Compressibility of Discrete and Finite Distributions Worst-case Compressibility of Discrete and Finite Distributions

2009-07-10

arxiv.org/abs/0907.1723v1

Computer Science

Information Theory

5 pages, 3 figures

Scientific paper

In the worst-case distributed source coding (DSC) problem of [1], the smaller cardinality of the support-set describing the correlation in informant data, may neither imply that fewer informant bits are required nor that fewer informants need to be queried, to finish the data-gathering at the sink. It is important to formally address these observations for two reasons: first, to develop good worst-case information measures and second, to perform meaningful worst-case information-theoretic analysis of various distributed data-gathering problems. Towards this goal, we introduce the notions of bit-compressibility and informant-compressibility of support-sets. We consider DSC and distributed function computation problems and provide results on computing the bit- and informant- compressibilities regions of the support-sets as a function of their cardinality.

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