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首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >Model criticism based on likelihood-free inference, with an application to protein network evolution
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Model criticism based on likelihood-free inference, with an application to protein network evolution

机译：基于无可能性推断的模型批评及其在蛋白质网络进化中的应用

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摘要

Mathematical models are an important tool to explain and comprehend complex phenomena, and unparalleled computational advances enable us to easily explore them without any or little understanding of their global properties. In fact, the likelihood of the data under complex stochastic models is often analytically or numerically intractable in many areas of sciences. This makes it even more important to simultaneously investigate the adequacy of these models-in absolute terms, against the data, rather than relative to the performance of other models-but no such procedure has been formally discussed when the likelihood is intractable. We provide a statistical interpretation to current developments in likelihood-free Bayesian inference that explicitly accounts for discrepancies between the model and the data, termed Approximate Bayesian Computation under model uncertainty (ABC_μ ). We augment the likelihood of the data with unknown error terms that correspond to freely chosen checking functions, and provide Monte Carlo strategies for sampling from the associated joint posterior distribution without the need of evaluating the likelihood. We discuss the benefit of incorporating model diagnostics within an ABC framework, and demonstrate how this method diagnoses model mismatch and guides model refinement by contrasting three qualitative models of protein network evolution to the protein interaction datasets of Helicobacter pylori and Treponema pal-lidum. Our results make a number of model deficiencies explicit, and suggest that the T. pallidum network topology is inconsistent with evolution dominated by link turnover or lateral gene transfer alone.
机译：数学模型是解释和理解复杂现象的重要工具，无与伦比的计算进展使我们能够轻松地探索它们，而无需了解它们的全局特性。实际上，在许多科学领域中，复杂随机模型下数据的可能性通常在分析或数值上都是棘手的。这使得同时调查这些模型的绝对性（相对于数据而言，绝对值而不是相对于其他模型的性能而言）显得尤为重要，但是在可能性难以解决时，尚未正式讨论过此类程序。我们对无可能性贝叶斯推理的最新发展提供了统计解释，该解释明确解释了模型与数据之间的差异，称为模型不确定性（ABC_μ）下的近似贝叶斯计算。我们使用与自由选择的检查函数相对应的未知误差项来增加数据的可能性，并提供了从相关联合后验分布进行采样的蒙特卡洛策略，而无需评估可能性。我们讨论了在ABC框架内合并模型诊断的好处，并展示了该方法如何通过将三种蛋白质网络进化的定性模型与幽门螺杆菌和梅毒螺旋体的蛋白质相互作用数据集进行对比来诊断模型不匹配并指导模型的完善。我们的结果表明了许多模型缺陷，并表明苍白锥虫网络拓扑结构与仅由链接更新或侧向基因转移主导的进化不一致。

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