Blogs (1) >>
POPL 2019
Sun 13 - Sat 19 January 2019 Cascais, Portugal
Wed 16 Jan 2019 13:45 - 14:07 at Sala I - Probabilistic Programming and Semantics Chair(s): Justin Hsu

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.

Wed 16 Jan
Times are displayed in time zone: Greenwich Mean Time : Belfast change

13:45 - 14:51: Probabilistic Programming and SemanticsResearch Papers at Sala I
Chair(s): Justin HsuUniversity of Wisconsin-Madison, USA
13:45 - 14:07
Probabilistic Programming with Densities in SlicStan: Efficient, Flexible and Deterministic
Research Papers
Maria I. GorinovaThe University of Edinburgh, Andrew D. GordonMicrosoft Research and University of Edinburgh, Charles SuttonUniversity of Edinburgh
Link to publication DOI Pre-print Media Attached File Attached
14:07 - 14:29
A Domain Theory for Statistical Probabilistic ProgrammingDistinguished Paper
Research Papers
Matthijs VákárUniversity of Oxford, Ohad KammarUniversity of Edinburgh, Sam StatonUniversity of Oxford
Link to publication DOI Pre-print Media Attached File Attached
14:29 - 14:51
Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling
Research Papers
Feras SaadMassachusetts Institute of Technology, Marco Cusumano-TownerMIT-CSAIL, Ulrich SchaechtleMassachusetts Institute of Technology, USA, Martin RinardMassachusetts Institute of Technology, Vikash MansinghkaMIT
Link to publication DOI Media Attached File Attached