Report from Dagstuhl Seminar 15181 Challenges and Trends in Probabilistic Programming

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. Seminar April 27–30, 2015 – http://www.dagstuhl.de/15181 1998 ACM Subject Classification D.1 Programming Techniques, D.2.4 Software/Program Verification, D.3 Programming Languages, D.4.6 Security and Protection, F. Theory of Computation, G.1.6 Optimization, G.3 Probability and Statistics, I.2.2 Automatic Programming, I.2.4 Knowledge Representation Formalisms and Methods