Replicated Computational Results (RCR) Report for “ProPPA: Probabilistic Programming for Stochastic Dynamical Systems”

Formal languages like process algebras have been shown to be effective tools in modelling a wide range of dynamic systems, providing a high-level description that is readily transformed into an executable model. However, their application is sometimes ...

In this paper, we investigate the semantic intricacies of conditioning in probabilistic programming, a major feature, e.g., in machine learning. We provide a quantitative weakest pre-condition semantics. In contrast to all other approaches, non-...

We develop the operational semantics of an untyped probabilistic λ-calculus with continuous distributions, and both hard and soft constraints,as a foundation for universal probabilistic programming languages such as Church, Anglican, and Venture. Our ...