Conference item
A domain theory for statistical probabilistic programming
- Abstract:
-
We give an adequate denotational semantics for languages with recursive higher-order types, continuous probability distributions, and soft constraints. These are expressive languages for building Bayesian models of the kinds used in computational statistics and machine learning. Among them are untyped languages, similar to Church and WebPPL, because our semantics allows recursive mixed-variance datatypes. Our semantics justifies important program equivalences including commutativity.
<...> Expand abstract
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Authors
Funding
Bibliographic Details
- Publisher:
- Association for Computing Machinery Publisher's website
- Host title:
- Proceedings of the ACM on Programming Languages
- Journal:
- 46th ACM SIGPLAN Symposium on Principles of Programming Languages Journal website
- Volume:
- 3
- Issue:
- POPL
- Article number:
- 36
- Publication date:
- 2019-01-02
- Acceptance date:
- 2018-11-09
- DOI:
- EISSN:
-
2475-1421
- ISSN:
-
2475-1421
Item Description
- Keywords:
- Pubs id:
-
pubs:941031
- UUID:
-
uuid:32d978e8-4a2a-4efb-915d-1e04c98d4502
- Local pid:
- pubs:941031
- Source identifiers:
-
941031
- Deposit date:
- 2018-11-12
Terms of use
- Copyright holder:
- Vákár et al
- Copyright date:
- 2019
- Notes:
- © 2019 Copyright held by the owner/author(s). This is a conference paper which will be presented at the 46th ACM SIGPLAN Symposium on Principles of Programming Languages (POPL 2019), 13-19 January 2019, Cascais/Lisbon, Portugal. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
- Licence:
- CC Attribution (CC BY)
Metrics
If you are the owner of this record, you can report an update to it here: Report update to this record