Learning probability by comparison
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Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks, PMLR 73:55, 2017.
Abstract
Learning probability by probabilistic modeling is a major task in statistical machine learning
and it has traditionally been supported by maximum likelihood estimation applied to
generative models or by a local maximizer applied to discriminative models. In this talk, we
introduce a third approach, an innovative one that learns probability by comparing probabilistic
events. In our approach, we give the ranking of probabilistic events and the system
learns a probability distribution so that the ranking is well respected. We implemented
this approach in PRISM, a logicbased probabilistic programming language, and conducted
learning experiments with real data for models described by PRISM programs.
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