Variable-order Bayesian Network : Bayesian Network, Graphical Model, Random Variable, Conditional Independence, Directed Acyclic Graph, Variable-order Markov Model, Markov Chain, Markov Property

Lambert M. Surhone

ISBN 10: 6130335539 / ISBN 13: 9786130335533

Edité par Omniscriptum, 2010

A propos de cet article

nach der Bestellung gedruckt Neuware - Printed after ordering - High Quality Content by WIKIPEDIA articles! Variable-order Bayesian network models provide an important extension of both the Bayesian network models and the variable-order Markov models. Variable-order Bayesian network models are used in machine learning in general and have shown great potential in bioinformatics applications. These models extend the widely-used position weight matrix models, Markov models, and Bayesian network models. In contrast to the BN models, where each random variable depends on a fixed subset of random variables, in Variable-order Bayesian network models these subsets may vary based on the specific realization of observed variables. The observed realizations are often called the context and, hence, Variable-order Bayesian network models are also known as context-specific Bayesian networks. The flexibility in the definition of conditioning subsets of variables turns out to be a real advantage in classification and analysis applications, as the statistical dependencies between random variables in a sequence of variables may be taken into account efficiently, and in a position-specific and context-specific manner.

N° de réf. du vendeur 9786130335533

Détails bibliographiques

• Titre: Variable-order Bayesian Network : Bayesian Network, Graphical Model, Random Variable, Conditional Independence, Directed Acyclic Graph, Variable-order Markov Model, Markov Chain, Markov Property
• Éditeur: Omniscriptum
• Date de parution: 2010
• Langue: anglais
• ISBN à 10 chiffres: 6130335539
• ISBN à 13 chiffres: 9786130335533
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 220 L x 150 H x 5 P (en millimètres) 220 millimètres de largeur par 150 millimètres de hauteur par 5 millimètres de profondeur
• Poids: 137 grammes

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Synopsis

Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Variable-order Bayesian network models provide an important extension of both the Bayesian network models and the variable-order Markov models. Variable-order Bayesian network models are used in machine learning in general and have shown great potential in bioinformatics applications. These models extend the widely-used position weight matrix models, Markov models, and Bayesian network models. In contrast to the BN models, where each random variable depends on a fixed subset of random variables, in Variable-order Bayesian network models these subsets may vary based on the specific realization of observed variables. The observed realizations are often called the context and, hence, Variable-order Bayesian network models are also known as context-specific Bayesian networks. The flexibility in the definition of conditioning subsets of variables turns out to be a real advantage in classification and analysis applications, as the statistical dependencies between random variables in a sequence of variables may be taken into account efficiently, and in a position-specific and context-specific manner.