Introducing statistical consistency for infinite chance constraints
العنوان: | Introducing statistical consistency for infinite chance constraints |
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المؤلفون: | Imen Zghidi, Abdelwaheb Rebai, Brahim Hnich |
المصدر: | Annals of Mathematics and Artificial Intelligence. 83:165-181 |
بيانات النشر: | Springer Science and Business Media LLC, 2018. |
سنة النشر: | 2018 |
مصطلحات موضوعية: | Infinite set, Mathematical optimization, 021103 operations research, Computer science, Applied Mathematics, 0211 other engineering and technologies, Inference, 02 engineering and technology, Range (mathematics), Artificial Intelligence, Consistency (statistics), 0202 electrical engineering, electronic engineering, information engineering, Statistical inference, Local consistency, 020201 artificial intelligence & image processing, Random variable, Constraint satisfaction problem |
الوصف: | In this paper, we propose a novel notion of statistical consistency for single-stage Stochastic Constraint Satisfaction Problems (SCSPs) in which some of the random variables’ support set is infinite. The essence of this novel notion of local consistency is to be able to make an inference in the presence of infinite scenarios in an uncertain environment. This inference would be based on a restricted finite subset of scenarios with a certain confidence level and a threshold tolerance error. The confidence level is the probability that characterizes the extend to which our inference — based on a subset of scenarios — is correct. The threshold tolerance error is the error range that we can tolerate while making such an inference. We propose a novel statistical consistency enforcing algorithm that is based on sound statistical inference; and experimentally show how to prune inconsistent values in the presence of an infinite set of scenarios. |
تدمد: | 1573-7470 1012-2443 |
URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::f88faab6e30f6748197017b5cb3bdd6a https://doi.org/10.1007/s10472-018-9572-3 |
حقوق: | CLOSED |
رقم الأكسشن: | edsair.doi...........f88faab6e30f6748197017b5cb3bdd6a |
قاعدة البيانات: | OpenAIRE |
تدمد: | 15737470 10122443 |
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