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dc.contributor.author
Abril Pla, Oriol
dc.contributor.author
Andreani, Virgile
dc.contributor.author
Carroll, Colin
dc.contributor.author
Dong, Larry
dc.contributor.author
Fonnesbeck, Christopher J.
dc.contributor.author
Kochurov, Maxim
dc.contributor.author
Kumar, Ravin
dc.contributor.author
Lao, Junpeng
dc.contributor.author
Luhmann, Christian C.
dc.contributor.author
Martín, Osvaldo Antonio
dc.contributor.author
Osthege, Michael
dc.contributor.author
Vieira, Ricardo
dc.contributor.author
Wiecki, Thomas
dc.contributor.author
Zinkov, Robert
dc.date.available
2024-04-15T09:42:44Z
dc.date.issued
2023-08
dc.identifier.citation
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
dc.identifier.issn
2376-5992
dc.identifier.uri
http://hdl.handle.net/11336/232919
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
PeerJ Inc.
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Bayesian statistics
dc.subject
Probabilistic programming
dc.subject
Python
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Markov chain Monte Carlo
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Statistical modeling
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BAYESIAN STATISTICS
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PROBABILISTIC PROGRAMMING
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PYTHON
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MARKOV CHAIN MONTE CARLO,
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STATISTICAL MODELING
dc.subject.classification
Ciencias de la Computación
dc.subject.classification
Ciencias de la Computación e Información
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.subject.classification
Estadística y Probabilidad
dc.subject.classification
Matemáticas
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
PyMC: a modern, and comprehensive probabilistic programming framework in Python
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2024-02-19T10:34:29Z
dc.journal.volume
9
dc.journal.pagination
1-36
dc.journal.pais
Estados Unidos
dc.description.fil
Fil: Abril Pla, Oriol. No especifíca;
dc.description.fil
Fil: Andreani, Virgile. Boston University; Estados Unidos
dc.description.fil
Fil: Carroll, Colin. Google Limited Liability Company (google Llc);
dc.description.fil
Fil: Dong, Larry. University of Toronto; Canadá
dc.description.fil
Fil: Fonnesbeck, Christopher J.. No especifíca;
dc.description.fil
Fil: Kochurov, Maxim. No especifíca;
dc.description.fil
Fil: Kumar, Ravin. Google Limited Liability Company (google Llc);
dc.description.fil
Fil: Lao, Junpeng. Google Limited Liability Company (google Llc);
dc.description.fil
Fil: Luhmann, Christian C.. State University of New York. Stony Brook University; Estados Unidos
dc.description.fil
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
dc.description.fil
Fil: Osthege, Michael. Helmholtz Gemeinschaft. Forschungszentrum Jülich; Alemania
dc.description.fil
Fil: Vieira, Ricardo. No especifíca;
dc.description.fil
Fil: Wiecki, Thomas. No especifíca;
dc.description.fil
Fil: Zinkov, Robert. University of Oxford; Reino Unido
dc.journal.title
PeerJ Computer Science
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.7717/peerj-cs.1516
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://peerj.com/articles/cs-1516/
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