Treffer: A simulated annealing-based method for learning bayesian networks from statistical data
Title:
A simulated annealing-based method for learning bayesian networks from statistical data
Authors:
Source:
Uncertainty processingInternational journal of intelligent systems. 21(3):335-348
Publisher Information:
New York, NY: Wiley, 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, Informatique théorique, Theoretical computing, Fonctions logiques, booléennes et de commutation, Logical, boolean and switching functions, Intelligence artificielle, Artificial intelligence, Apprentissage et systèmes adaptatifs, Learning and adaptive systems, Algorithme randomisé, Randomized algorithm, Algoritmo aleatorizado, Apprentissage probabilités, Probability learning, Aprendizaje probabilidades, Chaîne Markov, Markov chain, Cadena Markov, Donnée statistique, Statistical data, Dato estadístico, Modélisation, Modeling, Modelización, Méthode Monte Carlo, Monte Carlo method, Método Monte Carlo, Programmation discrète, Discrete programming, Programación discreta, Programmation mathématique, Mathematical programming, Programación matemática, Recuit simulé, Simulated annealing, Recocido simulado, Réseau Bayes, Bayes network, Red Bayes
Document Type:
Konferenz
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Institute of Information Theory and Automation, Prague, Czech Republic
ISSN:
0884-8173
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.17583031
Database:
PASCAL Archive
Weitere Informationen
The problem of learning Bayesian networks from statistical data is described and reformulated as a discrete optimization problem. For a solution we employ the stochastic algorithm that is known as simulated annealing and that is based on the Markov Chain Monte Carlo approach. Numerical examples are included to illustrate the efficiency of the method.