Bambi: A simple interface for fitting Bayesian linear models in Python

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorCapretto, Tomásen_US
dc.contributor.authorPiho, Camenen_US
dc.contributor.authorKumar, Ravinen_US
dc.contributor.authorWestfall, Jacoben_US
dc.contributor.authorYarkoni, Talen_US
dc.contributor.authorMartin, Osvaldoen_US
dc.contributor.departmentDepartment of Computer Scienceen
dc.contributor.groupauthorProbabilistic Machine Learningen
dc.contributor.groupauthorProfessorship Vehtari Akien
dc.contributor.organizationConsejo Nacional de Investigaciones Científicas y Técnicasen_US
dc.contributor.organizationrisQen_US
dc.contributor.organizationPyMC Labsen_US
dc.contributor.organizationBlackLokusen_US
dc.contributor.organizationUniversity of Texasen_US
dc.date.accessioned2022-10-19T06:46:38Z
dc.date.available2022-10-19T06:46:38Z
dc.date.issued2022-08-15en_US
dc.description.abstractThe popularity of Bayesian statistical methods has increased dramatically in recent years across many research areas and industrial applications. This is the result of a variety of methodological advances with faster and cheaper hardware as well as the development of new software tools. Here we introduce an open source Python package named Bambi (BAyesian Model Building Interface) that is built on top of the PyMC probabilistic programming framework and the ArviZ package for exploratory analysis of Bayesian models. Bambi makes it easy to specify complex generalized linear hierarchical models using a formula notation similar to those found in R. We demonstrate Bambi's versatility and ease of use with a few examples spanning a range of common statistical models including multiple regression, logistic regression, and mixed-effects modeling with crossed group specific effects. Additionally we discuss how automatic priors are constructed. Finally, we conclude with a discussion of our plans for the future development of Bambi.en
dc.description.versionPeer revieweden
dc.format.extent29
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationCapretto, T, Piho, C, Kumar, R, Westfall, J, Yarkoni, T & Martin, O 2022, ' Bambi: A simple interface for fitting Bayesian linear models in Python ', JOURNAL OF STATISTICAL SOFTWARE, vol. 103, no. 15 . https://doi.org/10.18637/jss.v103.i15en
dc.identifier.doi10.18637/jss.v103.i15en_US
dc.identifier.issn1548-7660
dc.identifier.otherPURE UUID: c5751ca3-01e2-474c-a1d3-5cbaa449d3d4en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/c5751ca3-01e2-474c-a1d3-5cbaa449d3d4en_US
dc.identifier.otherPURE LINK: https://arxiv.org/abs/2012.10754en_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/90939451/SCI_Capretto_etal_Bambi_JSS_2022.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/117275
dc.identifier.urnURN:NBN:fi:aalto-202210196063
dc.language.isoenen
dc.publisherFoundation for Open Access Statistics
dc.relation.ispartofseriesJOURNAL OF STATISTICAL SOFTWAREen
dc.relation.ispartofseriesVolume 103, issue 15en
dc.rightsopenAccessen
dc.subject.keywordBayesian statisticsen_US
dc.subject.keywordgeneralized linear modelsen_US
dc.subject.keywordmultilevel modelingen_US
dc.subject.keywordpythonen_US
dc.subject.keywordhierarchical Bayesian modelingen_US
dc.titleBambi: A simple interface for fitting Bayesian linear models in Pythonen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
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