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. © Springer Science+Business Media B.V. 2009.
CITATION STYLE
Ng, K. S., Lloyd, J. W., & Uther, W. T. B. (2008). Probabilistic modelling, inference and learning using logical theories. Annals of Mathematics and Artificial Intelligence, 54(1–3), 159–205. https://doi.org/10.1007/s10472-009-9136-7
Mendeley helps you to discover research relevant for your work.