The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Stan: A Probabilistic Programming Language for Bayesian Inference and Optimization
Abstract
Stan is a free and open-source C++ program that performs Bayesian inference or optimization for arbitrary user-specified models and can be called from the command line, R, Python, Matlab, or Julia and has great promise for fitting large and complex statistical models in many areas of application. We discuss Stan from users’ and developers’ perspectives and illustrate with a simple but nontrivial nonlinear regression example.
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Metadata
- Authors
- Gelman, A., Lee, D., & Guo, J.
- Venue
- Journal of Educational and Behavioral Statistics
- Published
- 2015-10
- Volume
- 40
- Issue
- 5
- Pages
- 530-543
- ISSN
- 1076-9986 (ISSN-L)
- 1935-1054
- OpenAlex
- W2102360969
- Cited by
- 540
- References
- 2
- FWCI
- 25.11
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
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- Gelman, A., Lee, D., & Guo, J. (2015). Stan: A Probabilistic Programming Language for Bayesian Inference and Optimization. Journal of Educational and Behavioral Statistics. https://doi.org/10.3102/1076998615606113
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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.
