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PyMC: a modern, and comprehensive probabilistic programming framework in Python
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, allowin...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10495961/ https://www.ncbi.nlm.nih.gov/pubmed/37705656 http://dx.doi.org/10.7717/peerj-cs.1516 |
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author | Abril-Pla, Oriol Andreani, Virgile Carroll, Colin Dong, Larry Fonnesbeck, Christopher J. Kochurov, Maxim Kumar, Ravin Lao, Junpeng Luhmann, Christian C. Martin, Osvaldo A. Osthege, Michael Vieira, Ricardo Wiecki, Thomas Zinkov, Robert |
author_facet | Abril-Pla, Oriol Andreani, Virgile Carroll, Colin Dong, Larry Fonnesbeck, Christopher J. Kochurov, Maxim Kumar, Ravin Lao, Junpeng Luhmann, Christian C. Martin, Osvaldo A. Osthege, Michael Vieira, Ricardo Wiecki, Thomas Zinkov, Robert |
author_sort | Abril-Pla, Oriol |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10495961 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104959612023-09-13 PyMC: a modern, and comprehensive probabilistic programming framework in Python Abril-Pla, Oriol Andreani, Virgile Carroll, Colin Dong, Larry Fonnesbeck, Christopher J. Kochurov, Maxim Kumar, Ravin Lao, Junpeng Luhmann, Christian C. Martin, Osvaldo A. Osthege, Michael Vieira, Ricardo Wiecki, Thomas Zinkov, Robert PeerJ Comput Sci Data Science 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. PeerJ Inc. 2023-09-01 /pmc/articles/PMC10495961/ /pubmed/37705656 http://dx.doi.org/10.7717/peerj-cs.1516 Text en © 2023 Abril-Pla et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Data Science Abril-Pla, Oriol Andreani, Virgile Carroll, Colin Dong, Larry Fonnesbeck, Christopher J. Kochurov, Maxim Kumar, Ravin Lao, Junpeng Luhmann, Christian C. Martin, Osvaldo A. Osthege, Michael Vieira, Ricardo Wiecki, Thomas Zinkov, Robert PyMC: a modern, and comprehensive probabilistic programming framework in Python |
title | PyMC: a modern, and comprehensive probabilistic programming framework in Python |
title_full | PyMC: a modern, and comprehensive probabilistic programming framework in Python |
title_fullStr | PyMC: a modern, and comprehensive probabilistic programming framework in Python |
title_full_unstemmed | PyMC: a modern, and comprehensive probabilistic programming framework in Python |
title_short | PyMC: a modern, and comprehensive probabilistic programming framework in Python |
title_sort | pymc: a modern, and comprehensive probabilistic programming framework in python |
topic | Data Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10495961/ https://www.ncbi.nlm.nih.gov/pubmed/37705656 http://dx.doi.org/10.7717/peerj-cs.1516 |
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