The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Probabilistic programming
Open accessAbstract
Probabilistic programs are usual functional or imperative programs with two added constructs: (1) the ability to draw values at random from distributions, and (2) the ability to condition values of variables in a program via observations. Models from diverse application areas such as computer vision, coding theory, cryptographic protocols, biology and reliability analysis can be written as probabilistic programs.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Cousot, P., & Cousot, R. (1977) · Proceedings of the 4th ACM SIGACT-SIGPLAN symposium on Principles of programming languages - POPL '77 · doi:10.1145/512950.512973 · Cited by 6,279
Fluctuations in the Abundance of a Species considered Mathematically1
VOLTERRA, V. (1926) · Nature · doi:10.1038/118558a0 · Cited by 2,054
A logic for reasoning about time and reliability
Hansson, H., & Jonsson, B. (1994) · Formal Aspects of Computing · doi:10.1007/bf01211866 · Cited by 1,372
Cited by
The frontier of simulation-based inference
Cranmer, K., Brehmer, J., & Louppe, G. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912789117 · Cited by 1,032
Kernel Mean Embedding of Distributions: A Review and Beyond
Muandet, K., Fukumizu, K., Sriperumbudur, B., & Schölkopf, B. (2017) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000060 · Cited by 409
Steps Toward Robust Artificial Intelligence
Dietterich, T. G. (2017) · AI Magazine · doi:10.1609/aimag.v38i3.2756 · Cited by 139
Scenic: a language for scenario specification and data generation
Fremont, D. J., Kim, E., Dreossi, T., Ghosh, S., Yue, X., Sangiovanni-Vincentelli, A. L., & Seshia, S. A. (2023) · Machine Learning · doi:10.1007/s10994-021-06120-5 · Cited by 112
FairSquare: probabilistic verification of program fairness
Albarghouthi, A., D'Antoni, L., Drews, S., & Nori, A. V. (2017) · Proceedings of the ACM on Programming Languages · doi:10.1145/3133904 · Cited by 94
Self-citation
Metadata
- Authors
- Gordon, A. D., Henzinger, T. A., Nori, A. V., & Rajamani, S. K.
- Venue
- Future of Software Engineering Proceedings
- Publisher
- ACM
- Published
- 2014-05-31
- Pages
- 167-181
- OpenAlex
- W2295266283
- Cited by
- 357
- References
- 3
- FWCI
- 20.16
- Type
- conference paper
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Probabilistic programming. https://app.cochat.ai/papers/10.1145/2593882.2593900
- The paper
- Gordon, A. D., Henzinger, T. A., Nori, A. V., & Rajamani, S. K. (2014). Probabilistic programming. Future of Software Engineering Proceedings. https://doi.org/10.1145/2593882.2593900
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
