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

Couverture souple

ISBN 10 : 6130335539 ISBN 13 : 9786130335533

Editeur : Betascript Publishing, 2010

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.

Détails de l'édition

• Éditeur: Betascript Publishing
• Date d'édition: 2010
• Langue: anglais
• ISBN 10: 6130335539
• ISBN 13: 9786130335533
• Reliure: Broché
• Nombre de pages: 80
• Éditeur: Surhone Lambert M., Timpledon Miriam T., Marseken Susan F.
• Coordonnées du fabricant: non disponible
• Personne responsable: non disponible