Statistical Inferences by Gaussian Markov Random Fields on Complex Networks

Author

Tanaka, Kazuyuki ; Usui, Takafumi ; Yasuda, Muneki

Author_Institution

Grad. Sch. of Inf. Sci., Tohoku Univ., Sendai, Japan

fYear

2008

fDate

10-12 Dec. 2008

Firstpage

214

Lastpage

219

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.

Keywords

Bayes methods; Gaussian processes; Markov processes; complex networks; inference mechanisms; learning (artificial intelligence); random processes; Bayesian statistics; Gaussian Markov random fields; complex networks; network structures; scale free networks; statistical inferences; Bayesian methods; Communication industry; Complex networks; Digital signal processing; Electronic mail; Gaussian processes; Information technology; Markov random fields; Performance analysis; Statistics; Bayes statistics; Gauss Markov random fields; complex network; graphical model; probabilistic inference; scale free network;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence for Modelling Control & Automation, 2008 International Conference on

Conference_Location

Vienna

Print_ISBN

978-0-7695-3514-2

Type

conf

DOI

10.1109/CIMCA.2008.14

Filename

5172627