Data from: The summary-likelihood method and its implementation in the Infusion package

In recent years, simulation methods such as approximate Bayesian computation have extensively been used to infer parameters of population genetic models where the likelihood is intractable. We describe an alternative approach, summary likelihood, that provides a likelihood-based analysis of the information retained in the summary statistics whose distribution is simulated. We provide an automated implementation as a standard R package, Infusion, and we test the method, in particular for a scenario of inference of population-size change from genetic data. We show that the method provides confidence intervals with controlled coverage independently of a prior distribution on parameters, in contrast to approximate Bayesian computation. We expect the method to be applicable for at least six-parameter models and discuss possible modifications for higher-dimensional inference problems.

近年来，诸如近似贝叶斯计算（approximate Bayesian computation）这类模拟方法，已被广泛应用于推断似然难以求解的群体遗传模型的参数。本文提出一种替代方法——摘要似然（summary likelihood），该方法可对模拟得到的摘要统计量中保留的信息开展基于似然的分析。我们将该方法实现为一款标准化R工具包Infusion，并针对从遗传数据推断群体大小变化的场景对方法进行了测试。研究表明，与近似贝叶斯计算不同，该方法可生成独立于参数先验分布的可控覆盖率置信区间。我们预期该方法至少可适用于六参数模型，并讨论了面向高维推断问题的可行改进方案。