Distributed Hypothesis Testing and the Output Statistics of Random Binning

Sun, Siming; Effros, Michelle

Permalink

https://hdl.handle.net/2142/130280 Copy

Description

Title

Distributed Hypothesis Testing and the Output Statistics of Random Binning

Author(s)

• Sun, Siming
• Effros, Michelle

Issue Date

2025-09-17

Keyword(s)

• Network information theory
• Source coding
• Hypothesis testing
• Random binning

Abstract

This paper studies a distributed hypothesis testing problem under communication constraints. Specifically, given a scenario where potentially dependent sources are available to a pair of independent devices, the aim is to understand whether a device that sees its own source precisely and a compressed description of the other source can reliably distinguish whether the two sources are distributed according to a known joint distribution or whether they are instead independent. This paper explores the behavior of the corresponding hypothesis test when compression employs random-binning encoder and considers the implication of the existence of such a test on the output statistics of a random binning source code. The extension to testing against other joint distributions with identical marginals as well as the indistinguishability between two distributions from overcompressed source descriptions are also discussed.

Publisher

Allerton Conference on Communication, Control, and Computing

Series/Report Name or Number

2025 61st Allerton Conference on Communication, Control, and Computing Proceedings

ISSN

2836-4503

Type of Resource

Text

Genre of Resource

Conference Paper/Presentation

Language

eng

Handle URL

https://hdl.handle.net/2142/130280&&

Copyright and License Information

Copyright 2025 is held by Siming Sun and Michelle Effros.

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