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

Wellcome Open Research

article

Jan Kokko¹  Ulpu Remes¹  Owen Thomas²  Henri Pesonen²  Jukka Corander¹ 

[1] Department of Mathematics and Statistics, University of Helsinki;Department of Biostatistics, University of Oslo;Parasites and Microbes, Wellcome Trust Sanger Institute

关键词: density-ratio estimation;    likelihood-free inference;    logistic regression;    summary statistics selection;   

DOI  :  10.12688/wellcomeopenres.15583.1

学科分类：内科医学

来源: Wellcome

【 摘 要 】

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.

【 授权许可】

CC BY