About: PyMC

An Entity of Type: software, from Named Graph: http://dbpedia.org, within Data Space: dbpedia.org

PyMC (formerly known as PyMC3) is a Python package for Bayesian statistical modeling and probabilistic machine learning which focuses on advanced Markov chain Monte Carlo and variational fitting algorithms.It is a rewrite from scratch of the previous version of the PyMC software.Unlike PyMC2, which had used Fortran extensions for performing computations, PyMC relies on Aesara, a Python library that allows to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays.From version 3.8 PyMC relies on ArviZ to handle plotting, diagnostics, and statistical checks. PyMC and Stan are the two most popular probabilistic programming tools.PyMC is an open source project, developed by the community and fiscally sponsored by .

Property Value
dbo:abstract
  • PyMC (formerly known as PyMC3) is a Python package for Bayesian statistical modeling and probabilistic machine learning which focuses on advanced Markov chain Monte Carlo and variational fitting algorithms.It is a rewrite from scratch of the previous version of the PyMC software.Unlike PyMC2, which had used Fortran extensions for performing computations, PyMC relies on Aesara, a Python library that allows to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays.From version 3.8 PyMC relies on ArviZ to handle plotting, diagnostics, and statistical checks. PyMC and Stan are the two most popular probabilistic programming tools.PyMC is an open source project, developed by the community and fiscally sponsored by . PyMC has been used to solve inference problems in several scientific domains, includingastronomy, epidemiology,molecular biology,crystallography,chemistry,ecologyand psychology.Previous versions of PyMC were also used widely, for example inclimate science,public health, neuroscience,and parasitology. After Theano announced plans to discontinue development in 2017, the PyMC team evaluated TensorFlow Probability as a computational backend, but decided in 2020 to take over the development of Theano.Large parts of the Theano codebase have been refactored and compilation through JAX and Numba were added.The PyMC team has released the revised computational backend under the name Aesara and continue the development of PyMC. (en)
  • PyMC（曾叫做PyMC3）是一个Python包，用于贝叶斯统计建模和概率机器学习，它聚焦于高级马尔可夫链蒙特卡洛法和变分拟合算法。 (zh)
dbo:computingPlatform
dbo:genre
dbo:latestReleaseDate
  • 2022-09-19 (xsd:date)
dbo:latestReleaseVersion
  • 4.2.0
dbo:license
dbo:operatingSystem
dbo:programmingLanguage
dbo:releaseDate
  • 2013-05-04 (xsd:date)
dbo:wikiPageExternalLink
dbo:wikiPageID
  • 53242993 (xsd:integer)
dbo:wikiPageLength
  • 14920 (xsd:nonNegativeInteger)
dbo:wikiPageRevisionID
  • 1111810663 (xsd:integer)
dbo:wikiPageWikiLink
dbp:author
  • PyMC Development Team (en)
dbp:genre
dbp:latestReleaseDate
  • 2022-09-19 (xsd:date)
dbp:latestReleaseVersion
  • 4.200000 (xsd:double)
dbp:license
dbp:name
  • PyMC (en)
dbp:operatingSystem
dbp:platform
dbp:programmingLanguage
dbp:released
  • 2013-05-04 (xsd:date)
dbp:repo
dbp:website
dbp:wikiPageUsesTemplate
dct:subject
rdf:type
rdfs:comment
  • PyMC（曾叫做PyMC3）是一个Python包，用于贝叶斯统计建模和概率机器学习，它聚焦于高级马尔可夫链蒙特卡洛法和变分拟合算法。 (zh)
  • PyMC (formerly known as PyMC3) is a Python package for Bayesian statistical modeling and probabilistic machine learning which focuses on advanced Markov chain Monte Carlo and variational fitting algorithms.It is a rewrite from scratch of the previous version of the PyMC software.Unlike PyMC2, which had used Fortran extensions for performing computations, PyMC relies on Aesara, a Python library that allows to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays.From version 3.8 PyMC relies on ArviZ to handle plotting, diagnostics, and statistical checks. PyMC and Stan are the two most popular probabilistic programming tools.PyMC is an open source project, developed by the community and fiscally sponsored by . (en)
rdfs:label
  • PyMC (en)
  • PyMC (zh)
owl:sameAs
prov:wasDerivedFrom
foaf:homepage
foaf:isPrimaryTopicOf
foaf:name
  • PyMC (en)
is dbo:wikiPageRedirects of
is dbo:wikiPageWikiLink of
is foaf:primaryTopic of
Powered by OpenLink Virtuoso    This material is Open Knowledge     W3C Semantic Web Technology     This material is Open Knowledge    Valid XHTML + RDFa
This content was extracted from Wikipedia and is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License