## Probabilistic Modelling, Inference and Learning using Logical Theories

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by
K. S. Ng
,
J. W. Lloyd
,
W. T. B. Uther

Citations: | 9 - 3 self |

### BibTeX

@MISC{Ng_probabilisticmodelling,,

author = {K. S. Ng and J. W. Lloyd and W. T. B. Uther},

title = {Probabilistic Modelling, Inference and Learning using Logical Theories},

year = {}

}

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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.