TY - JOUR
AU1 - Ng, K.
AU2 - Lloyd, J.
AU3 - Uther, W.
AB - 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.
TI - Probabilistic modelling, inference and learning using logical theories
JF - Annals of Mathematics and Artificial Intelligence
DO - 10.1007/s10472-009-9136-7
DA - 2009-04-17
UR - https://www.deepdyve.com/lp/springer-journals/probabilistic-modelling-inference-and-learning-using-logical-theories-0MUoIJ4tY0
SP - 159
EP - 205
VL - 54
IS - 3
DP - DeepDyve
ER -