Title :
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
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;
Conference_Titel :
Computational Intelligence for Modelling Control & Automation, 2008 International Conference on
Conference_Location :
Vienna
Print_ISBN :
978-0-7695-3514-2
DOI :
10.1109/CIMCA.2008.14
