Learning probability by comparison

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Taisuke Sato ;
Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks, PMLR 73:5-5, 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 logic-based probabilistic programming language, and conducted learning experiments with real data for models described by PRISM programs.

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