Kokko Jan, Remes Ulpu, Thomas Owen, Pesonen Henri, Corander Jukka
Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.
Department of Biostatistics, University of Oslo, Oslo, Norway.
Wellcome Open Res. 2019 Dec 10;4:197. doi: 10.12688/wellcomeopenres.15583.1. eCollection 2019.
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.
基于模拟器模型的无似然推断是统计学中一个新兴的方法论分支，在群体遗传学、天文学和经济学等不同领域的应用中受到了相当大的关注。最近，统计分类器的能力已被用于无似然推断，以获得模型参数的点估计甚至后验分布。在这里，我们介绍PYLFIRE，这是一种使用惩罚逻辑回归的推断方法LFIRE（通过比率估计进行无似然推断）的开源Python实现。PYLFIRE作为通用ELFI推断软件http://elfi.ai的一部分提供，以使无似然推断的用户和开发者社区都受益。
