PYLFIRE : Python implementation of likelihood-free inference by ratio estimation

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dc.contributor.author Kokko, J.
dc.contributor.author Remes, U.
dc.contributor.author Thomas, Owen
dc.contributor.author Pesonen, H.
dc.contributor.author Corander, J.
dc.date.accessioned 2021-02-10T23:17:39Z
dc.date.available 2021-02-10T23:17:39Z
dc.date.issued 2019-12-10
dc.identifier.citation Kokko , J , Remes , U , Thomas , O , Pesonen , H & Corander , J 2019 , ' PYLFIRE : Python implementation of likelihood-free inference by ratio estimation ' , Wellcome open research , vol. 4 , 197 . https://doi.org/10.12688/wellcomeopenres.15583.1
dc.identifier.other PURE: 160236363
dc.identifier.other PURE UUID: 7d6cc5d9-7ac9-428f-9bab-16b1ff77f213
dc.identifier.other RIS: urn:7E6F86AB030F4F79F267823953CA6EAA
dc.identifier.other Scopus: 85083672169
dc.identifier.other ORCID: /0000-0003-1435-0207/work/88677673
dc.identifier.uri http://hdl.handle.net/10138/326329
dc.description Export Date: 10 February 2021 Correspondence Address: Kokko, J.; Department of Mathematics and Statistics, Finland; email: jan.kokko@helsinki.fi
dc.description.abstract Likelihood-free inference for simulator-based models is an emerging methodological branch of statistics which has attracted considerable attention in applications across diverse fields such as population genetics, astronomy and economics. Recently, the power of statistical classifiers has been harnessed in likelihood-free inference to obtain either point estimates or even posterior distributions of model parameters. Here we introduce PYLFIRE, an open-source Python implementation of the inference method LFIRE (likelihood-free inference by ratio estimation) that uses penalised logistic regression. PYLFIRE is made available as part of the general ELFI inference software http://elfi.ai to benefit both the user and developer communities for likelihood-free inference. © 2019 Kokko J et al. en
dc.format.extent 13
dc.language.iso eng
dc.relation.ispartof Wellcome open research
dc.rights cc_by
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject 112 Statistics and probability
dc.subject density-ratio estimation
dc.subject likelihood-free inference
dc.subject logistic regression
dc.subject summary statistics selection
dc.title PYLFIRE : Python implementation of likelihood-free inference by ratio estimation en
dc.type Article
dc.contributor.organization Department of Mathematics and Statistics
dc.contributor.organization Jukka Corander / Principal Investigator
dc.contributor.organization Biostatistics Helsinki
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.12688/wellcomeopenres.15583.1
dc.relation.issn 2398-502X
dc.rights.accesslevel openAccess
dc.type.version publishedVersion

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