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

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http://hdl.handle.net/10138/326329

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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

Title: PYLFIRE : Python implementation of likelihood-free inference by ratio estimation
Author: Kokko, J.; Remes, U.; Thomas, Owen; Pesonen, H.; Corander, J.
Contributor: University of Helsinki, Department of Mathematics and Statistics
University of Helsinki, Department of Mathematics and Statistics
University of Helsinki, Jukka Corander / Principal Investigator
Date: 2019-12-10
Language: eng
Number of pages: 13
Belongs to series: Wellcome open research
ISSN: 2398-502X
URI: http://hdl.handle.net/10138/326329
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.
Subject: 112 Statistics and probability
density-ratio estimation
likelihood-free inference
logistic regression
summary statistics selection
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