A Domain Theory for Statistical Probabilistic Programming
- Submitting institution
-
University of Edinburgh
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 84913445
- Type
- D - Journal article
- DOI
-
10.1145/3290349
- Title of journal
- Proceedings of the ACM on Programming Languages (PACMPL)
- Article number
- 36
- First page
- -
- Volume
- 3
- Issue
- POPL
- ISSN
- 2475-1421
- Open access status
- Compliant
- Month of publication
- January
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
C - Foundations of Computation
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- First to combine known-to-be-problematic language features, despite much prior community effort (30 years). Combines fundamental ideas from statistics (randomisation, Riesz representation), domain theory, and semantics. POPL'19 (acceptance rate 29%) Distinguished Paper Award (top 8% of accepted papers). Invited talk at the International Domains'19 workshop. Coauthor Vákár co-develops the STAN modelling language (10,000+ direct and indirect users), and extended this work to automatic-differentiation and recursion (arXiv 2007.05282). A precursor for validation of recursive inference algorithm implementations used in STAN, formed the basis for a successful Facebook Research Award funding application in 2019.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -