ArviZ a unified library for exploratory analysis of Bayesian models in Python

Autores
Kumar, Ravin; Carroll, Colin; Hartikainen, Ari; Martín, Osvaldo Antonio
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
ArviZ is a Python package for exploratory analysis of Bayesian models. ArviZ aims to be a package that integrates seamlessly with established probabilistic programming languages like PyStan, PyMC, Edward, emcee, Pyro and easily integrated with novel or bespoke Bayesian analyses. Where the aim of the probabilistic programming languages is to make it easy to build and solve Bayesian models, the aim of the ArviZ library is to make it easy to process and analyze the results from the Bayesian models.
Fil: Kumar, Ravin. No especifíca;
Fil: Carroll, Colin. No especifíca;
Fil: Hartikainen, Ari. Aalto University; Finlandia
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
Materia
BAYESIAN STATISTICS
VISUALIZATION
PYTHON
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/114615
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/114615
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling ArviZ a unified library for exploratory analysis of Bayesian models in PythonKumar, RavinCarroll, ColinHartikainen, AriMartín, Osvaldo AntonioBAYESIAN STATISTICSVISUALIZATIONPYTHONhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1https://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1ArviZ is a Python package for exploratory analysis of Bayesian models. ArviZ aims to be a package that integrates seamlessly with established probabilistic programming languages like PyStan, PyMC, Edward, emcee, Pyro and easily integrated with novel or bespoke Bayesian analyses. Where the aim of the probabilistic programming languages is to make it easy to build and solve Bayesian models, the aim of the ArviZ library is to make it easy to process and analyze the results from the Bayesian models.Fil: Kumar, Ravin. No especifíca;Fil: Carroll, Colin. No especifíca;Fil: Hartikainen, Ari. Aalto University; FinlandiaFil: 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"; ArgentinaJournal of Open Source Software2019-01-15info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/114615Kumar, Ravin; Carroll, Colin; Hartikainen, Ari; Martín, Osvaldo Antonio; ArviZ a unified library for exploratory analysis of Bayesian models in Python; Journal of Open Source Software; Journal of Open Source Software; 4; 33; 15-1-2019; 1143-11472475-9066CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://joss.theoj.org/papers/10.21105/joss.01143info:eu-repo/semantics/altIdentifier/doi/10.21105/joss.01143info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:56:42Zoai:ri.conicet.gov.ar:11336/114615instacron: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 09:56:42.525CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv ArviZ a unified library for exploratory analysis of Bayesian models in Python
title ArviZ a unified library for exploratory analysis of Bayesian models in Python
spellingShingle ArviZ a unified library for exploratory analysis of Bayesian models in Python
Kumar, Ravin
BAYESIAN STATISTICS
VISUALIZATION
PYTHON
title_short ArviZ a unified library for exploratory analysis of Bayesian models in Python
title_full ArviZ a unified library for exploratory analysis of Bayesian models in Python
title_fullStr ArviZ a unified library for exploratory analysis of Bayesian models in Python
title_full_unstemmed ArviZ a unified library for exploratory analysis of Bayesian models in Python
title_sort ArviZ a unified library for exploratory analysis of Bayesian models in Python
dc.creator.none.fl_str_mv Kumar, Ravin
Carroll, Colin
Hartikainen, Ari
Martín, Osvaldo Antonio
author Kumar, Ravin
author_facet Kumar, Ravin
Carroll, Colin
Hartikainen, Ari
Martín, Osvaldo Antonio
author_role author
author2 Carroll, Colin
Hartikainen, Ari
Martín, Osvaldo Antonio
author2_role author
author
author
dc.subject.none.fl_str_mv BAYESIAN STATISTICS
VISUALIZATION
PYTHON
topic BAYESIAN STATISTICS
VISUALIZATION
PYTHON
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv ArviZ is a Python package for exploratory analysis of Bayesian models. ArviZ aims to be a package that integrates seamlessly with established probabilistic programming languages like PyStan, PyMC, Edward, emcee, Pyro and easily integrated with novel or bespoke Bayesian analyses. Where the aim of the probabilistic programming languages is to make it easy to build and solve Bayesian models, the aim of the ArviZ library is to make it easy to process and analyze the results from the Bayesian models.
Fil: Kumar, Ravin. No especifíca;
Fil: Carroll, Colin. No especifíca;
Fil: Hartikainen, Ari. Aalto University; Finlandia
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
description ArviZ is a Python package for exploratory analysis of Bayesian models. ArviZ aims to be a package that integrates seamlessly with established probabilistic programming languages like PyStan, PyMC, Edward, emcee, Pyro and easily integrated with novel or bespoke Bayesian analyses. Where the aim of the probabilistic programming languages is to make it easy to build and solve Bayesian models, the aim of the ArviZ library is to make it easy to process and analyze the results from the Bayesian models.
publishDate 2019
dc.date.none.fl_str_mv 2019-01-15
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/11336/114615
Kumar, Ravin; Carroll, Colin; Hartikainen, Ari; Martín, Osvaldo Antonio; ArviZ a unified library for exploratory analysis of Bayesian models in Python; Journal of Open Source Software; Journal of Open Source Software; 4; 33; 15-1-2019; 1143-1147
2475-9066
CONICET Digital
CONICET
url http://hdl.handle.net/11336/114615
identifier_str_mv Kumar, Ravin; Carroll, Colin; Hartikainen, Ari; Martín, Osvaldo Antonio; ArviZ a unified library for exploratory analysis of Bayesian models in Python; Journal of Open Source Software; Journal of Open Source Software; 4; 33; 15-1-2019; 1143-1147
2475-9066
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://joss.theoj.org/papers/10.21105/joss.01143
info:eu-repo/semantics/altIdentifier/doi/10.21105/joss.01143
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Journal of Open Source Software
publisher.none.fl_str_mv Journal of Open Source Software
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
_version_ 1844613701504598016
score 13.070432

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