Challenges and Trends in Probabilistic Programming: (Dagstuhl Seminar 15181)

Gilles Barthe (Editor), Andrew D. Gordon (Editor), Joost-Pieter Katoen (Editor), Annabelle McIver (Editor)

Research output: Contribution to journalArticle


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.
Original languageEnglish
Pages (from-to)123-141
Number of pages19
JournalDagstuhl Reports
Issue number4
Publication statusPublished - 2015

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