License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagRep.5.4.123
URN: urn:nbn:de:0030-drops-53536
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2015/5353/
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Barthe, Gilles ; Gordon, Andrew D. ; Katoen, Joost-Pieter ; McIver, Annabelle
Weitere Beteiligte (Hrsg. etc.): Gilles Barthe and Andrew D. Gordon and Joost-Pieter Katoen and Annabelle McIver

Challenges and Trends in Probabilistic Programming (Dagstuhl Seminar 15181)

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dagrep_v005_i004_p123_s15181.pdf (0.8 MB)

Abstract

This report documents the program and the outcomes of Dagstuhl Seminar 15181 "Challenges and Trends in Probabilistic Programming". Probabilistic programming is at the heart of machine learning for describing distribution functions; Bayesian inference is pivotal in their analysis. Probabilistic programs are used in security for describing both cryptographic constructions (such as randomised encryption) and security experiments. In addition, probabilistic models are an active research topic in quantitative information now. Quantum programs are inherently probabilistic due to the random outcomes of quantum measurements. Finally, there is a rapidly growing interest in program analysis of probabilistic programs, whether it be using model checking, theorem proving, static analysis, or similar. Dagstuhl Seminar 15181 brought researchers from these various research communities together so as to exploit synergies and realize cross-fertilisation.

BibTeX - Entry

@Article{barthe_et_al:DR:2015:5353,
  author =	{Gilles Barthe and Andrew D. Gordon and Joost-Pieter Katoen and Annabelle McIver},
  title =	{{Challenges and Trends in Probabilistic Programming (Dagstuhl Seminar 15181)}},
  pages =	{123--141},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2015},
  volume =	{5},
  number =	{4},
  editor =	{Gilles Barthe and Andrew D. Gordon and Joost-Pieter Katoen and Annabelle McIver},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2015/5353},
  URN =		{urn:nbn:de:0030-drops-53536},
  doi =		{10.4230/DagRep.5.4.123},
  annote =	{Keywords: Bayesian networks, differential privacy, machine learning, probabilistic programs, security, semantics, static analysis, verification}
}

Keywords: Bayesian networks, differential privacy, machine learning, probabilistic programs, security, semantics, static analysis, verification
Collection: Dagstuhl Reports, Volume 5, Issue 4
Issue Date: 2015
Date of publication: 16.12.2015

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