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 (u. a.) | Taschenbuch | Englisch | 2026 | OmniScriptum | EAN 9786130335533 | Verantwortliche Person f�r die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabr�ck, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.

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T�tulo

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

Editor

OmniScriptum

Fecha de publicaci�n

2026

Idioma

Ingl�s

ISBN 10

6130335539

ISBN 13

9786130335533

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Taschenbuch

Estado

Neu

Dimensiones

Peso

137 gramos

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Sinopsis

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.

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