Result: Operations for inference in continuous Bayesian networks with linear deterministic variables
Title:
Operations for inference in continuous Bayesian networks with linear deterministic variables
Authors:
Source:
PGM'04: 2nd European Workshop on Probabilistic Graphical Models, Leiden, October 4-8, 2004International journal of approximate reasoning. 42(1-2):21-36
Publisher Information:
Amsterdam: Elsevier, 2006.
Publication Year:
2006
Physical Description:
print, 12 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Apprentissage et systèmes adaptatifs, Learning and adaptive systems, Approche déterministe, Deterministic approach, Enfoque determinista, Approche probabiliste, Probabilistic approach, Enfoque probabilista, Densité, Density, Densidad, Estimation Bayes, Bayes estimation, Estimación Bayes, Fonction densité, Density function, Función densidad, Fonction répartition, Distribution function, Función distribución, Inférence, Inference, Inferencia, Loi conditionnelle, Conditional distribution, Ley condicional, Loi conjointe, Joint distribution, Ley conjunta, Loi normale, Gaussian distribution, Curva Gauss, Modèle linéaire, Linear model, Modelo lineal, Non déterminisme, Non determinism, No determinismo, Processus Gauss, Gaussian process, Proceso Gauss, Réseau Bayes, Bayes network, Red Bayes, Système non déterministe, Non deterministic system, Sistema no determinista, Système paramètre réparti, Distributed parameter system, Sistema parámetro repartido, Bayesian networks, Conditional linear Gaussian models, Deterministic variables
Document Type:
Conference
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Economics and Business, Virginia Military Institute, Lexington, VA 24450, United States
School of Business, University of Kansas, 1300 Sunnyside Ave., Summerfield Hall, Lawrence, KS 66045-7585, United States
School of Business, University of Kansas, 1300 Sunnyside Ave., Summerfield Hall, Lawrence, KS 66045-7585, United States
ISSN:
0888-613X
Rights:
Copyright 2006 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.17714078
Database:
PASCAL Archive
Further Information
An important class of continuous Bayesian networks are those that have linear conditionally deterministic variables (a variable that is a linear deterministic function of its parents). In this case, the joint density function for the variables in the network does not exist. Conditional linear Gaussian (CLG) distributions can handle such cases when all variables are normally distributed. In this paper, we develop operations required for performing inference with linear conditionally deterministic variables in continuous Bayesian networks using relationships derived from joint cumulative distribution functions. These methods allow inference in networks with linear deterministic variables and non-Gaussian distributions.