A domain theory for statistical probabilistic programming
- Submitting institution
-
University of Oxford
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2068
- Type
- D - Journal article
- DOI
-
-
- Title of journal
- Proceedings of the ACM on Programming Languages
- Article number
- 36
- First page
- 1
- Volume
- 3
- Issue
- POPL
- ISSN
- 2475-1421
- Open access status
- Compliant
- Month of publication
- January
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
https://dl.acm.org/doi/10.1145/3290349#sec-supp
- 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)
-
-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- PCAMPL is a journal that publishes peer-reviewed full versions of papers originating in POPL. This work was awarded a POPL 2019 Distinguished Paper award. "At most 10% of the accepted papers of POPL 2019 will be designated as Distinguished Papers. This award highlights papers that the POPL program committee thinks should be read by a broad audience due to their relevance, originality, significance and clarity." Six of 77 accepted papers received this designation (https://tinyurl.com/yfbjwbdg). This work is the theoretical basis for the paper �P??NK:Functional Probabilistic NetKAT� by Vandenbroucke and Schrijvers, PACMPL 2020 (https://doi.org/10.1145/3371107).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -