PyMC: a modern, and comprehensive probabilistic programming framework in Python

Autores
Abril Pla, Oriol; Andreani, Virgile; Carroll, Colin; Dong, Larry; Fonnesbeck, Christopher J.; Kochurov, Maxim; Kumar, Ravin; Lao, Junpeng; Luhmann, Christian C.; Martín, Osvaldo Antonio; Osthege, Michael; Vieira, Ricardo; Wiecki, Thomas; Zinkov, Robert
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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.
Fil: Abril Pla, Oriol. No especifíca;
Fil: Andreani, Virgile. Boston University; Estados Unidos
Fil: Carroll, Colin. Google Limited Liability Company (google Llc);
Fil: Dong, Larry. University of Toronto; Canadá
Fil: Fonnesbeck, Christopher J.. No especifíca;
Fil: Kochurov, Maxim. No especifíca;
Fil: Kumar, Ravin. Google Limited Liability Company (google Llc);
Fil: Lao, Junpeng. Google Limited Liability Company (google Llc);
Fil: Luhmann, Christian C.. State University of New York. Stony Brook University; Estados Unidos
Fil: Martín, Osvaldo Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias Físico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; Argentina
Fil: Osthege, Michael. Helmholtz Gemeinschaft. Forschungszentrum Jülich; Alemania
Fil: Vieira, Ricardo. No especifíca;
Fil: Wiecki, Thomas. No especifíca;
Fil: Zinkov, Robert. University of Oxford; Reino Unido
Materia
Bayesian statistics
Probabilistic programming
Python
Markov chain Monte Carlo
Statistical modeling
BAYESIAN STATISTICS
PROBABILISTIC PROGRAMMING
PYTHON
MARKOV CHAIN MONTE CARLO,
STATISTICAL MODELING
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/232919
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/232919
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling PyMC: a modern, and comprehensive probabilistic programming framework in PythonAbril Pla, OriolAndreani, VirgileCarroll, ColinDong, LarryFonnesbeck, Christopher J.Kochurov, MaximKumar, RavinLao, JunpengLuhmann, Christian C.Martín, Osvaldo AntonioOsthege, MichaelVieira, RicardoWiecki, ThomasZinkov, RobertBayesian statisticsProbabilistic programmingPythonMarkov chain Monte CarloStatistical modelingBAYESIAN STATISTICSPROBABILISTIC PROGRAMMINGPYTHONMARKOV CHAIN MONTE CARLO,STATISTICAL MODELINGhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1https://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1PyMC 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.Fil: Abril Pla, Oriol. No especifíca;Fil: Andreani, Virgile. Boston University; Estados UnidosFil: Carroll, Colin. Google Limited Liability Company (google Llc);Fil: Dong, Larry. University of Toronto; CanadáFil: Fonnesbeck, Christopher J.. No especifíca;Fil: Kochurov, Maxim. No especifíca;Fil: Kumar, Ravin. Google Limited Liability Company (google Llc);Fil: Lao, Junpeng. Google Limited Liability Company (google Llc);Fil: Luhmann, Christian C.. State University of New York. Stony Brook University; Estados UnidosFil: Martín, Osvaldo Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias Físico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; ArgentinaFil: Osthege, Michael. Helmholtz Gemeinschaft. Forschungszentrum Jülich; AlemaniaFil: Vieira, Ricardo. No especifíca;Fil: Wiecki, Thomas. No especifíca;Fil: Zinkov, Robert. University of Oxford; Reino UnidoPeerJ Inc.2023-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/232919Abril Pla, Oriol; Andreani, Virgile; Carroll, Colin; Dong, Larry; Fonnesbeck, Christopher J.; et al.; PyMC: a modern, and comprehensive probabilistic programming framework in Python; PeerJ Inc.; PeerJ Computer Science; 9; 8-2023; 1-362376-5992CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.7717/peerj-cs.1516info:eu-repo/semantics/altIdentifier/url/https://peerj.com/articles/cs-1516/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:41:32Zoai:ri.conicet.gov.ar:11336/232919instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:41:33.542CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv PyMC: a modern, and comprehensive probabilistic programming framework in Python
title PyMC: a modern, and comprehensive probabilistic programming framework in Python
spellingShingle PyMC: a modern, and comprehensive probabilistic programming framework in Python
Abril Pla, Oriol
Bayesian statistics
Probabilistic programming
Python
Markov chain Monte Carlo
Statistical modeling
BAYESIAN STATISTICS
PROBABILISTIC PROGRAMMING
PYTHON
MARKOV CHAIN MONTE CARLO,
STATISTICAL MODELING
title_short 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_sort PyMC: a modern, and comprehensive probabilistic programming framework in Python
dc.creator.none.fl_str_mv Abril Pla, Oriol
Andreani, Virgile
Carroll, Colin
Dong, Larry
Fonnesbeck, Christopher J.
Kochurov, Maxim
Kumar, Ravin
Lao, Junpeng
Luhmann, Christian C.
Martín, Osvaldo Antonio
Osthege, Michael
Vieira, Ricardo
Wiecki, Thomas
Zinkov, Robert
author Abril Pla, Oriol
author_facet Abril Pla, Oriol
Andreani, Virgile
Carroll, Colin
Dong, Larry
Fonnesbeck, Christopher J.
Kochurov, Maxim
Kumar, Ravin
Lao, Junpeng
Luhmann, Christian C.
Martín, Osvaldo Antonio
Osthege, Michael
Vieira, Ricardo
Wiecki, Thomas
Zinkov, Robert
author_role author
author2 Andreani, Virgile
Carroll, Colin
Dong, Larry
Fonnesbeck, Christopher J.
Kochurov, Maxim
Kumar, Ravin
Lao, Junpeng
Luhmann, Christian C.
Martín, Osvaldo Antonio
Osthege, Michael
Vieira, Ricardo
Wiecki, Thomas
Zinkov, Robert
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Bayesian statistics
Probabilistic programming
Python
Markov chain Monte Carlo
Statistical modeling
BAYESIAN STATISTICS
PROBABILISTIC PROGRAMMING
PYTHON
MARKOV CHAIN MONTE CARLO,
STATISTICAL MODELING
topic Bayesian statistics
Probabilistic programming
Python
Markov chain Monte Carlo
Statistical modeling
BAYESIAN STATISTICS
PROBABILISTIC PROGRAMMING
PYTHON
MARKOV CHAIN MONTE CARLO,
STATISTICAL MODELING
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv 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.
Fil: Abril Pla, Oriol. No especifíca;
Fil: Andreani, Virgile. Boston University; Estados Unidos
Fil: Carroll, Colin. Google Limited Liability Company (google Llc);
Fil: Dong, Larry. University of Toronto; Canadá
Fil: Fonnesbeck, Christopher J.. No especifíca;
Fil: Kochurov, Maxim. No especifíca;
Fil: Kumar, Ravin. Google Limited Liability Company (google Llc);
Fil: Lao, Junpeng. Google Limited Liability Company (google Llc);
Fil: Luhmann, Christian C.. State University of New York. Stony Brook University; Estados Unidos
Fil: Martín, Osvaldo Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias Físico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; Argentina
Fil: Osthege, Michael. Helmholtz Gemeinschaft. Forschungszentrum Jülich; Alemania
Fil: Vieira, Ricardo. No especifíca;
Fil: Wiecki, Thomas. No especifíca;
Fil: Zinkov, Robert. University of Oxford; Reino Unido
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.
publishDate 2023
dc.date.none.fl_str_mv 2023-08
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/232919
Abril Pla, Oriol; Andreani, Virgile; Carroll, Colin; Dong, Larry; Fonnesbeck, Christopher J.; et al.; PyMC: a modern, and comprehensive probabilistic programming framework in Python; PeerJ Inc.; PeerJ Computer Science; 9; 8-2023; 1-36
2376-5992
CONICET Digital
CONICET
url http://hdl.handle.net/11336/232919
identifier_str_mv Abril Pla, Oriol; Andreani, Virgile; Carroll, Colin; Dong, Larry; Fonnesbeck, Christopher J.; et al.; PyMC: a modern, and comprehensive probabilistic programming framework in Python; PeerJ Inc.; PeerJ Computer Science; 9; 8-2023; 1-36
2376-5992
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.7717/peerj-cs.1516
info:eu-repo/semantics/altIdentifier/url/https://peerj.com/articles/cs-1516/
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv PeerJ Inc.
publisher.none.fl_str_mv PeerJ Inc.
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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