Fisher information determinant and stochastic complexity for Markov models

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2 Citations (Scopus)

Abstract

We study Fisher information of stationary Markov models with a finite alphabet. In particular, we derive the Fisher information determinant of expectation parameter η, which is defined as expectation of Markov type. The Fisher information determinant with respect to Markov kernel parameter (conditional probabilities) is easy to find, while it is not so with respect to the expectation parameter η nor the natural parameter θ. Note that θ and η are of special importance for exponential families including Markov models.

Original languageEnglish
Title of host publication2009 IEEE International Symposium on Information Theory, ISIT 2009
Pages1894-1898
Number of pages5
DOIs
Publication statusPublished - Nov 19 2009
Event2009 IEEE International Symposium on Information Theory, ISIT 2009 - Seoul, Korea, Republic of
Duration: Jun 28 2009Jul 3 2009

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8102

Other

Other2009 IEEE International Symposium on Information Theory, ISIT 2009
CountryKorea, Republic of
CitySeoul
Period6/28/097/3/09

All Science Journal Classification (ASJC) codes

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

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