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Multiple Objects: Error Exponents in Hypotheses Testing and Identification

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Information Theory, Combinatorics, and Search Theory

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7777))

Abstract

We survey a series of investigations of optimal testing of multiple hypotheses concerning various multiobject models.

These studies are a prominent instance of application of methods and techniques developed in Shannon information theory for solution of typical statistical problems.

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Author information

Authors and Affiliations

  1. Institute for Informatics and Automation Problems, National Academy of Sciences of the Republic of Armenia, Armenia

    Evgueni Haroutunian & Parandzem Hakobyan

Authors
  1. Evgueni Haroutunian
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  2. Parandzem Hakobyan
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Editors and Affiliations

  1. Department of Mathematics, University of Bielefeld, Universitätsstr. 25, 33615, Bielefeld, Germany

    Harout Aydinian  & Christian Deppe  & 

  2. Department of Computer Science, University of Salerno, via Ponte don Melillo, 84084, Fisciano, Italy

    Ferdinando Cicalese

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Haroutunian, E., Hakobyan, P. (2013). Multiple Objects: Error Exponents in Hypotheses Testing and Identification. In: Aydinian, H., Cicalese, F., Deppe, C. (eds) Information Theory, Combinatorics, and Search Theory. Lecture Notes in Computer Science, vol 7777. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36899-8_14

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  • DOI: https://doi.org/10.1007/978-3-642-36899-8_14

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  • Print ISBN: 978-3-642-36898-1

  • Online ISBN: 978-3-642-36899-8

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