Variable-order Bayesian Network
(Sprache: Englisch)
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...
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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 statisticaldependencies between random variables in a sequence of variables may be taken into account efficiently, and in a position-specific and context-specific manner.
Bibliographische Angaben
- 2010, 84 Seiten, Maße: 22 cm, Kartoniert (TB), Englisch
- Herausgeber: Marseken, Susan F., Surhone, Lambert M., Timpledon, Miriam T.
- Verlag: Betascript Publishing
- ISBN-10: 6130335539
- ISBN-13: 9786130335533
Sprache:
Englisch
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