Statistical inferences by gaussian markov random fields on complex networks

Kazuyuki Tanaka, Takafumi Usui, Muneki Yasuda

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

Abstract

Gaussian Markov random fields are applied to many statistical inferences. Probabilistic models of statistical inferences are constructed in the concept of Bayesian statistics and have some network structures. In the present paper, we analyze the statistical performance of the statistical inferences in Gaussian Markov random fields on some complex networks including scale free networks. We discuss efficiency of scale free networks for statistical inferences of Gauss Markov random fields.

Original languageEnglish
Title of host publication2008 International Conference on Computational Intelligence for Modelling Control and Automation, CIMCA 2008
Pages214-219
Number of pages6
DOIs
Publication statusPublished - 2008 Dec 1
Event2008 International Conference on Computational Intelligence for Modelling Control and Automation, CIMCA 2008 - Vienna, Austria
Duration: 2008 Dec 102008 Dec 12

Publication series

Name2008 International Conference on Computational Intelligence for Modelling Control and Automation, CIMCA 2008

Other

Other2008 International Conference on Computational Intelligence for Modelling Control and Automation, CIMCA 2008
CountryAustria
CityVienna
Period08/12/1008/12/12

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Software
  • Control and Systems Engineering

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