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Variable-order Bayesian Network
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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.
Weitere Informationen
- Allgemeine Informationen
- GTIN 09786130335533
- Editor Lambert M. Surhone, Miriam T. Timpledon, Susan F. Marseken
- Sprache Englisch
- Größe H5mm x B220mm x T150mm
- Jahr 2010
- EAN 9786130335533
- Format Fachbuch
- ISBN 978-613-0-33553-3
- Titel Variable-order Bayesian Network
- Untertitel Bayesian Network, Graphical Model, Random Variable, Conditional Independence, Directed Acyclic Graph, Variable-order Markov Model, Markov Chain, Markov Property
- Gewicht 132g
- Herausgeber Betascript Publishers
- Anzahl Seiten 84
- Genre Mathematik
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