License: Creative Commons Attribution 3.0 Unported license (CC-BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ICALP.2018.3
URN: urn:nbn:de:0030-drops-90073
URL: https://drops.dagstuhl.de/opus/volltexte/2018/9007/
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Staton, Sam

Probability Theory from a Programming Perspective (Invited Paper)

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LIPIcs-ICALP-2018-3.pdf (0.2 MB)


Abstract

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.

BibTeX - Entry

@InProceedings{staton:LIPIcs:2018:9007,
  author =	{Sam Staton},
  title =	{{Probability Theory from a Programming Perspective (Invited Paper)}},
  booktitle =	{45th International Colloquium on Automata, Languages, and  Programming (ICALP 2018)},
  pages =	{3:1--3:1},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-076-7},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{107},
  editor =	{Ioannis Chatzigiannakis and Christos Kaklamanis and D{\'a}niel Marx and Donald Sannella},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2018/9007},
  URN =		{urn:nbn:de:0030-drops-90073},
  doi =		{10.4230/LIPIcs.ICALP.2018.3},
  annote =	{Keywords: correctness, complexity, statistics}
}

Keywords: correctness, complexity, statistics
Collection: 45th International Colloquium on Automata, Languages, and Programming (ICALP 2018)
Issue Date: 2018
Date of publication: 04.07.2018


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