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  • 标题:The EfProb Library for Probabilistic Calculations
  • 本地全文:下载
  • 作者:Kenta Cho ; Bart Jacobs
  • 期刊名称:LIPIcs : Leibniz International Proceedings in Informatics
  • 电子版ISSN:1868-8969
  • 出版年度:2017
  • 卷号:72
  • 页码:25:1-25:8
  • DOI:10.4230/LIPIcs.CALCO.2017.25
  • 出版社:Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
  • 摘要:EfProb is an abbreviation of Effectus Probability. It is the name of a library for probability calculations in Python. EfProb offers a uniform language for discrete, continuous and quantum probability. For each of these three cases, the basic ingredients of the language are states, predicates, and channels. Probabilities are typically calculated as validities of predicates in states. States can be updated (conditioned) with predicates. Channels can be used for state transformation and for predicate transformation. This short paper gives an overview of the use of EfProb.
  • 关键词:probability; embedded language; effectus theory
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