The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
PyMC: a modern, and comprehensive probabilistic programming framework in Python
Open accessAbstract
PyMC is a probabilistic programming library for Python that provides tools for constructing and fitting Bayesian models. It offers an intuitive, readable syntax that is close to the natural syntax statisticians use to describe models. PyMC leverages the symbolic computation library PyTensor, allowing it to be compiled into a variety of computational backends, such as C, JAX, and Numba, which in turn offer access to different computational architectures including CPU, GPU, and TPU. Being a general modeling framework, PyMC supports a variety of models including generalized hierarchical linear regression and classification, time series, ordinary differential equations (ODEs), and non-parametric models such as Gaussian processes (GPs). We demonstrate PyMC’s versatility and ease of use with examples spanning a range of common statistical models. Additionally, we discuss the positive role of PyMC in the development of the open-source ecosystem for probabilistic programming.
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SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0686-2 · Cited by 38,430
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Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2019) · Apollo - University of Cambridge Repository · doi:10.17863/cam.64134 · Cited by 10,460
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Metadata
- Authors
- Abril-Pla, O., Andreani, V., Carroll, C., Dong, L., Fonnesbeck, C. J., Kochurov, M., Kumar, R., Lao, J., Luhmann, C. C., Martin, O. A., Osthege, M., Vieira, R., Wiecki, T., & Zinkov, R.
- Venue
- PeerJ Computer Science
- Publisher
- PeerJ
- Published
- 2023-09-01
- Volume
- 9
- Pages
- e1516
- ISSN
- 2376-5992 (ISSN-L)
- OpenAlex
- W4386373863
- Cited by
- 870
- References
- 46
- FWCI
- 132.43
- Type
- journal article
- Language
- en
- Open access
- gold
- Funders
- NumFOCUS
- PyMC Labs
- National Agency of Scientific and Technological Promotion ANPCyT (PICT-02212)
- National Scientific and Technical Research Council CONICET (PIP-0087)
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- Abril-Pla, O., Andreani, V., Carroll, C., Dong, L., Fonnesbeck, C. J., Kochurov, M., Kumar, R., Lao, J., Luhmann, C. C., Martin, O. A., Osthege, M., Vieira, R., Wiecki, T., & Zinkov, R. (2023). PyMC: a modern, and comprehensive probabilistic programming framework in Python. PeerJ Computer Science. https://doi.org/10.7717/peerj-cs.1516
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