A Convenient Category for Higher-Order Probability Theory
- Submitting institution
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University of Edinburgh
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 58246604
- Type
- E - Conference contribution
- DOI
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10.1109/LICS.2017.8005137
- Title of conference / published proceedings
- 2017 32nd Annual ACM/IEEE Symposium on Logic in Computer Science (LICS)
- First page
- 1
- Volume
- -
- Issue
- -
- ISSN
- 1043-6871
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2017
- URL
-
-
- Supplementary information
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- 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
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3
- Research group(s)
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C - Foundations of Computation
- Citation count
- 6
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Greatly simplifies recent solution of longstanding (50+ years) open problem: rigorous models for higher-order probability theory with continuous distributions. Shows that Bayesian regression gains cleaner expression using function spaces. Generalizes the famous De Finetti theorem from probability theory with completely rigorous proofs. Highest-rated paper at LICS2017 by reviewers. Invited to submit extended version in JACM. Invited for plenary talks at STOC2018 and Uncertainty in Computation programme at Simons-Berkeley Institute. Foundation for later work on Bayesian probabilistic programming languages (POPL18+19, ICFP18, LICS2018): has influenced probabilistic PCF and probabilistic call-by-push-value.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -