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.
Original language | English |
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Pages (from-to) | 245-268 |
Number of pages | 23 |
Journal | Annals of Mathematics and Artificial Intelligence |
Volume | 32 |
Issue number | 1 |
DOIs | |
Publication status | Published - 2001 |
Keywords
- probability
- statistics
- vine dependence
- markov tree
- management theory