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

 
9786130335533: Variable-order Bayesian Network: Bayesian Network, Graphical Model, Random Variable, Conditional Independence, Directed Acyclic Graph, Variable-order Markov Model, Markov Chain, Markov Property
Softcover
ISBN 10: 6130335539 ISBN 13: 9786130335533
Verlag: Betascript Publishers, 2010

Inhaltsangabe

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.

Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.

Reseña del editor

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.

„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.

Verlag
Betascript Publishers
Erscheinungsdatum
2010
Sprache
Englisch
ISBN 10 
6130335539
ISBN 13 
9786130335533
Einband
Tapa blanda
Anzahl der Seiten
80
Herausgeber
Surhone Lambert M., Timpledon Miriam T., Marseken Susan F.
Kontakt zum Hersteller
Nicht verfügbar
Verantwortliche Person
Nicht verfügbar