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

J. Kokko, U. Remes, Owen Thomas, H. Pesonen, J. Corander

Research output: Contribution to journalArticleScientificpeer-review

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.
Original languageEnglish
Article number197
JournalWellcome open research
Volume4
Number of pages13
ISSN2398-502X
DOIs
Publication statusPublished - 10 Dec 2019
MoE publication typeA1 Journal article-refereed

Bibliographical note

Export Date: 10 February 2021

Correspondence Address: Kokko, J.; Department of Mathematics and Statistics, Finland; email: [email protected]

Fields of Science

  • 112 Statistics and probability

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