Robust detection and social learning in tandem networks

Jack Ho, Wee Peng Tay, Tony Q.S. Quek

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

We consider a binary hypothesis testing problem in a tandem network where the distribution of the agent observations under each hypothesis comes from an uncertainty class. When agents know their positions in the tandem, and the contamination of the uncertainty classes are non-zero, we show that asymptotic learning of the true hypothesis under social learning is not possible even when the log likelihood ratio of the nominal distributions of the uncertainty classes is unbounded. Furthermore, asymptotic learning in social learning is achievable if and only if the uncertainty classes contamination converge to zero. When agents do not know their positions, the minimax error probability is bounded from zero, and we provide tight bounds for it.

Original languageEnglish
Title of host publication2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5457-5461
Number of pages5
ISBN (Print)9781479928927
DOIs
Publication statusPublished - 2014
Event2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy
Duration: 2014 May 42014 May 9

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
CountryItaly
CityFlorence
Period14-05-0414-05-09

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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