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文章基本信息

  • 标题:Probability Theory from a Programming Perspective (Invited Paper)
  • 作者:Sam Staton
  • 期刊名称:LIPIcs : Leibniz International Proceedings in Informatics
  • 电子版ISSN:1868-8969
  • 出版年度:2018
  • 卷号:107
  • 页码:3:1-3:1
  • DOI:10.4230/LIPIcs.ICALP.2018.3
  • 出版社:Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
  • 摘要:A leading idea is to apply techniques from verification and programming theory to machine learning and statistics, to deal with things like compositionality and various notions of correctness and complexity. Probabilistic programming is an example of this. Moreover, this approach leads to new foundational methods in probability theory. This is particularly true in the "non-parametric" aspects, for example in higher-order functions and infinite random graph models.
  • 关键词:correctness; complexity; statistics
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