Factor metanetwork: a multilevel probabilistic meta-model based on factor graphs

Factor graph (FG) is a recently developed graphical model. It subsumes many other graphical models, including Bayesian networks and Markov networks. This paper presents a multilevel probabilistic meta-model based on FGs-factor metanetwork. The model is a kind of context-enabled networks, and assumes...

Full description

Saved in:
Bibliographic Details
Published in:International journal of general systems 2007-08, Vol.36 (4), p.465-477
Main Authors: Shi, Dongyu, You, Jinyuan
Format: Article
Language:English
Subjects:
Factor graphs
Graphical models
Metanetwork
Sum-product algorithm
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Factor graph (FG) is a recently developed graphical model. It subsumes many other graphical models, including Bayesian networks and Markov networks. This paper presents a multilevel probabilistic meta-model based on FGs-factor metanetwork. The model is a kind of context-enabled networks, and assumes that interoperability between component networks can be also modelled by another FG. It is composed of a set of FGs, which are put on each other in such a way that the probability distributions of single or multiple variables (function nodes) of every previous network (factor graph) depend on probability distributions associated with nodes of the next network. We assume parameters of function nodes in a FG as random variables, and allow all kinds of conditional dependencies between these probability functions. Several cases of two-level factor metanetworks are presented, and an example is presented to show the model's application.
ISSN:0308-1079
1563-5104
DOI:10.1080/03081070600971922