| 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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