کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
397814 1438508 2008 19 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Belief update in CLG Bayesian networks with lazy propagation
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Belief update in CLG Bayesian networks with lazy propagation
چکیده انگلیسی

In recent years, Bayesian networks with a mixture of continuous and discrete variables have received an increasing level of attention. In this paper, we focus on the restricted class of mixture Bayesian networks known as conditional linear Gaussian Bayesian networks (CLG Bayesian networks) and present an architecture for exact belief update for this class of mixture networks.The proposed architecture is an extension of lazy propagation using operations of Lauritzen and Jensen [S.L. Lauritzen, F. Jensen, Stable local computation with mixed Gaussian distributions, Statistics and Computing 11(2) (2001) 191–203] and Cowell [R.G. Cowell, Local propagation in conditional Gaussian Bayesian networks, Journal of Machine Learning Research 6 (2005) 1517–1550]. By decomposing clique and separator potentials into sets of factors, the proposed architecture takes advantage of independence and irrelevance properties induced by the structure of the graph and the evidence. The resulting benefits are illustrated by examples and assessed by experiments.The performance of the proposed architecture has been evaluated using a set of randomly generated networks. The results indicate a significant potential of the proposed architecture.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Approximate Reasoning - Volume 49, Issue 2, October 2008, Pages 503-521