Probabilistic modelling, inference and learning using logical theories

  • Authors:
  • K. S. Ng;J. W. Lloyd;W. T. Uther

  • Affiliations:
  • National ICT Australia and The Australian National University, Acton, Australia;College of Engineering and Computer Science, The Australian National University, Acton, Australia;National ICT Australia and University of New South Wales, Kensington, Australia

  • Venue:
  • Annals of Mathematics and Artificial Intelligence
  • Year:
  • 2008

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Abstract

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.