Probabilistic Modelling, Inference and Learning using Logical Theories
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.
|Collections||ANU Research Publications|
|Source:||Annals of Mathematics and Artificial Intelligence|
|01_Ng_Probabilistic_Modelling,_2008.pdf||1.78 MB||Adobe PDF||Request a copy|
|02_Ng_Probabilistic_Modelling,_2008.pdf||125.77 kB||Adobe PDF||Request a copy|
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