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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
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.
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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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