Analysis of bin models with applications in coding theory

Research output: Contribution to journalConference articlepeer-review

Abstract

Bin models represent one of the most frequently used descriptive representations of phenomena as diverse as spreading of disease, financial market fluctuation, error burst generation in communication channels, or learning in neurological systems. When analyzing randomized bin models, it is usually of interest to evaluate some statistic depending on the characteristics of the distribution of objects (balls) into bins. Due to the inherent mutual dependence of the occupancy variables, determining this statistic may represent a challenging analytical task. In this paper, we describe a class of invertible probabilistic transforms that result in mapping dependent bin occupancies into independent random variables. The statistics of interest can be evaluated in the transform domain and then appropriately inverted to obtain an exact solution. Or, for problems with large values for the parameters, the asymptotic behavior of the statistics can be deduced from the transform itself. Possible analytical applications of these new transform techniques in coding theory include the binning schemes related to Luby Transform (LT), Slepian-Wolf, and deletion-error correcting coding.

Original languageEnglish (US)
Number of pages1
JournalIEEE International Symposium on Information Theory - Proceedings
StatePublished - Oct 20 2004
Externally publishedYes
EventProceedings - 2004 IEEE International Symposium on Information Theory - Chicago, IL, United States
Duration: Jun 27 2004Jul 2 2004

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

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