Shannon's mutual information measures the degree of mutual dependence between two random variables. Two related information functionals have also been developed in the literature: Multiinformation, a multivariate extension of mutual information; and lautum information, the Csiszár conjugate of mutual information. In this work, we define illum information, the multivariate extension of lautum information and the Csiszár conjugate of multiinformation. We provide operational interpretations of this functional, including in the problem of independence testing of a set of random variables. Further, we also provide informational characterizations of illum information such as the data processing inequality and the chain rule for distributions on tree-structured graphical models. Finally, as illustrative examples, we compute the illum information for Ising models and Gauss-Markov random fields.

Original languageEnglish (US)
Title of host publication2017 Information Theory and Applications Workshop, ITA 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509052936
StatePublished - Aug 30 2017
Event2017 Information Theory and Applications Workshop, ITA 2017 - San Diego, United States
Duration: Feb 12 2017Feb 17 2017

Publication series

Name2017 Information Theory and Applications Workshop, ITA 2017


Other2017 Information Theory and Applications Workshop, ITA 2017
Country/TerritoryUnited States
CitySan Diego

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Software
  • Computational Theory and Mathematics


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