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.
@InProceedings{pmlr-v73-sato17a,
title = {Learning probability by comparison},
author = {Taisuke Sato},
booktitle = {Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks},
pages = {5--5},
year = {2017},
editor = {Antti Hyttinen and Joe Suzuki and Brandon Malone},
volume = {73},
series = {Proceedings of Machine Learning Research},
address = {},
month = {20--22 Sep},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v73/sato17a/sato17a.pdf},
url = {http://proceedings.mlr.press/v73/sato17a.html},
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.}
}
%0 Conference Paper
%T Learning probability by comparison
%A Taisuke Sato
%B Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks
%C Proceedings of Machine Learning Research
%D 2017
%E Antti Hyttinen
%E Joe Suzuki
%E Brandon Malone
%F pmlr-v73-sato17a
%I PMLR
%J Proceedings of Machine Learning Research
%P 5--5
%U http://proceedings.mlr.press
%V 73
%W PMLR
%X 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.
Sato, T.. (2017). Learning probability by comparison. Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks, in PMLR 73:5-5
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