标题： 当模型失效：引力波天文学中后验预测检验与模型误设的介绍 显示英文标题

标题： When models fail: an introduction to posterior predictive checks and model misspecification in gravitational-wave astronomy

摘要： 贝叶斯推断是引力波天文学中的强大工具。 它使我们能够推断合并致密天体双星的特性，并确定这些合并在质量、自旋和红移方面的分布情况。 随着越来越多的关键结果使用贝叶斯推断得出，对贝叶斯方法的审查也在增加。 在本综述中，我们讨论了\textit{模型误指配}现象，即由于假设模型的缺陷，使用贝叶斯推断得到的结果具有误导性。 这些缺陷可能会阻碍我们对描述物理系统的真正参数的推断。 它们还可能降低我们区分“最佳拟合”模型的能力：如果两个模型都是对现实的明显不良描述，那么说模型 A 比模型 B 更受青睐可能是有误导性的。 总的来说，模型失败有两种方式：无法充分描述数据（信号或噪声）的模型具有错误指定的似然函数。 人口模型——例如，用于描述黑洞质量分布的模型——可能由于先验的错误指定而无法充分描述真实的人口。 我们推荐了一些测试和检查方法，这些方法对于通过受引力波天文学启发的例子来发现错误指定的模型是有用的。 我们还包括配套的 python 笔记本来说明基本概念。 显示英文摘要

摘要： Bayesian inference is a powerful tool in gravitational-wave astronomy. It enables us to deduce the properties of merging compact-object binaries and to determine how these mergers are distributed as a population according to mass, spin, and redshift. As key results are increasingly derived using Bayesian inference, there is increasing scrutiny on Bayesian methods. In this review, we discuss the phenomenon of \textit{model misspecification}, in which results obtained with Bayesian inference are misleading because of deficiencies in the assumed model(s). Such deficiencies can impede our inferences of the true parameters describing physical systems. They can also reduce our ability to distinguish the "best fitting" model: it can be misleading to say that Model~A is preferred over Model~B if both models are manifestly poor descriptions of reality. Broadly speaking, there are two ways in which models fail: models that fail to adequately describe the data (either the signal or the noise) have misspecified likelihoods. Population models -- designed, for example, to describe the distribution of black hole masses -- may fail to adequately describe the true population due to a misspecified prior. We recommend tests and checks that are useful for spotting misspecified models using examples inspired by gravitational-wave astronomy. We include companion python notebooks to illustrate essential concepts.