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Probabilistic reasoning with terms.

Title:Probabilistic reasoning with terms.
Authors: Peter Flach, Elias Gyftodimos, and Nicolas Lachiche
Series:Linköping Electronic Articles in Computer and Information Science
ISSN 1401-9841
Issue:Vol. 7(2002): nr 011
URL: http://www.ep.liu.se/ea/cis/2002/011/

Abstract: Many problems in artificial intelligence can be naturally approached by generating and manipulating probability distributions over structured objects. First-order terms such as lists, trees and tuples and nestings thereof can represent individuals with complex structure in the underlying domain, such as sequences or molecules. Higher-order terms such as sets and multisets provide additional representational flexibility. I will present two Bayesian approaches that employ such probability distributions over structured objects: the first is an upgrade of the well-known naive Bayesian classifier to deal with first-order and higher-order terms, and the second is an upgrade of propositional Bayesian networks to deal with nested tuples.

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