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Probabilistic Modelling, Inference and Learning using Logical Theories

Ng, Kee Siong; Lloyd, John; Uther, William T.B.


This paper provides a study of probabilistic modelling, inference and learning in a logic-based setting. We show how probability densities, being functions, can be represented and reasoned with naturally and directly in higher-order logic, an expressive formalism not unlike the (informal) everyday language of mathematics. We give efficient inference algorithms and illustrate the general approach with a diverse collection of applications. Some learning issues are also considered.

CollectionsANU Research Publications
Date published: 2008
Type: Journal article
Source: Annals of Mathematics and Artificial Intelligence
DOI: 10.1007/s10472-009-9136-7


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