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，已被广泛用于推断似然函数难以求解的群体遗传模型的参数。本文提出一种替代方法——汇总似然（summary likelihood），该方法可对模拟得到的汇总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的R软件包Infusion，并完成了方法测试，尤其针对利用遗传数据推断种群规模变化的场景开展了验证。研究表明，与近似贝叶斯计算不同，该方法所生成的置信区间的覆盖概率可控，且不受参数先验分布的影响。我们预计该方法至少可适用于六参数模型，并讨论了针对高维推断问题的可行改进方案。