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Lang."],"published-print":{"date-parts":[[2019,1,2]]},"abstract":"<jats:p>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.<\/jats:p>\n                  <jats:p>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.<\/jats:p>","DOI":"10.1145\/3290348","type":"journal-article","created":{"date-parts":[[2019,1,4]],"date-time":"2019-01-04T08:33:51Z","timestamp":1546590831000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Probabilistic programming with densities in SlicStan: efficient, flexible, and deterministic"],"prefix":"10.1145","volume":"3","author":[{"given":"Maria I.","family":"Gorinova","sequence":"first","affiliation":[{"name":"University of Edinburgh, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew D.","family":"Gordon","sequence":"additional","affiliation":[{"name":"Microsoft Research, UK \/ University of Edinburgh, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charles","family":"Sutton","sequence":"additional","affiliation":[{"name":"Google Brain, USA \/ University of Edinburgh, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2019,1,2]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/292540.292555"},{"key":"e_1_2_2_2_1","first-page":"265","article-title":"TensorFlow: A System for Large-Scale Machine Learning","volume":"16","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi , Paul Barham , Jianmin Chen , Zhifeng Chen , Andy Davis , Jeffrey Dean , Matthieu Devin , Sanjay Ghemawat , Geoffrey Irving , Michael Isard , 2016 . 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