Probabilistic reasoning with terms
Peter A. Flach, Elias Gyftodimos, Nicolas Lachiche · 2003
Many problems in artificial intelligence can be naturally approached by generating and manipulating probability distributions over structured objects. In this paper we represent structured objects by first-order logic terms (lists, trees, tuples, and nestings thereof) and higher-order terms (sets, multisets), and we study the question how to define probability distributions over such terms. We 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.