PYLFIRE: Python implementation of likelihood-free inference by ratio estimation [version 1; peer review: 2 approved]

• Main Authors: Jan Kokko, Ulpu Remes, Owen Thomas, Henri Pesonen, Jukka Corander
• Format: Article
• Language: English
• Published: Wellcome 2019-12-01
• Series: Wellcome Open Research
• Online Access: https://wellcomeopenresearch.org/articles/4-197/v1

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.