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Probability density decomposition for conditionally dependent random variables modeled by Vines

Bedford, T.J. and Cooke, R. (2001) Probability density decomposition for conditionally dependent random variables modeled by Vines. Annals of Mathematics and Artificial Intelligence, 32 (1). pp. 245-268. ISSN 1012-2443

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Abstract

A vine is a new graphical model for dependent random variables. Vines generalize the Markov trees often used in modeling multivariate distributions. They differ from Markov trees and Bayesian belief nets in that the concept of conditional independence is weakened to allow for various forms of conditional dependence. A general formula for the density of a vine dependent distribution is derived. This generalizes the well-known density formula for belief nets based on the decomposition of belief nets into cliques. Furthermore, the formula allows a simple proof of the Information Decomposition Theorem for a regular vine. The problem of (conditional) sampling is discussed, and Gibbs sampling is proposed to carry out sampling from conditional vine dependent distributions. The so-called lsquocanonical vinesrsquo built on highest degree trees offer the most efficient structure for Gibbs sampling.

Item type: Article
ID code: 4335
Keywords: probability, statistics, vine dependence, markov tree, management theory, Management. Industrial Management, Statistics, Artificial Intelligence, Applied Mathematics
Subjects: Social Sciences > Industries. Land use. Labor > Management. Industrial Management
Social Sciences > Statistics
Department: Strathclyde Business School > Management Science
Related URLs:
    Depositing user: Strathprints Administrator
    Date Deposited: 19 Oct 2007
    Last modified: 04 Sep 2014 14:16
    URI: http://strathprints.strath.ac.uk/id/eprint/4335

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