PPS 2016 - Workshop on probabilistic programming semantics

Probabilistic programming is the idea of expressing probabilistic models and inference methods as programs and transformations, to ease use and reuse. The recent rise of practical implementations as well as research activity in probabilistic programming has renewed the need for semantics to help us share insights and innovations.
This workshop aims to bring programming-language and machine-learning researchers together to advance the semantic foundations of probabilistic programming. Topics include but are not limited to:
the denotational semantics of probabilistic functions, open universe, loops, and conditioning;
the operational semantics of sampling, exact inference, and MCMC transitions;
axiomatic and equational reasoning;
types and polymorphism;
and last but not least, how semantics informs any aspect of probabilistic programming, be it design, theory, implementation, or applications.