Stan is a probabilistic programming language that has been increasingly used for real-world scalable projects. However, to make practical inference possible, the language sacrifices some of its usability by adopting a block syntax, which lacks compositionality and flexible user-defined functions. Moreover, the semantics of the language has been mainly given in terms of intuition about implementation, and has not been formalised. This paper provides a formal treatment of the Stan language, and introduces the probabilistic programming language SlicStan --- a compositional, self-optimising version of Stan. Our main contributions are (1) the formalisation of a core subset of Stan through an operational density-based semantics; (2) the design and semantics of the Stan-like language SlicStan, which facilities better code reuse and abstraction through its compositional syntax, more flexible functions, and information-flow type system; and (3) a formal, semantic-preserving procedure for translating SlicStan to Stan.

@article{paperbot690,
  title = {Probabilistic programming with densities in SlicStan: efficient, flexible, and deterministic},
  author = {Maria I. Gorinova and Andrew D. Gordon and Charles Sutton},
  journal = {Proceedings of the ACM on Programming Languages},
  volume = {3},
  number = {POPL},
  year = {2019},
  doi = {10.1145/3290348}
}