Neural density estimators have proven remarkably powerful in performing efficient simulation-based Bayesian inference in various research domains. In particular, the BayesFlow framework uses a two-step approach to enable amortized parameter estimation in settings where the likelihood function is implicitly defined by a simulation program. But how faithful is such inference when simulations are poor representations of reality? In this paper, we conceptualize the types of model misspecification arising in simulation-based inference and systematically investigate the performance of the BayesFlow framework under these misspecifications. We propose an augmented optimization objective which imposes a probabilistic structure on the latent data space and utilize maximum mean discrepancy (MMD) to detect potentially catastrophic misspecifications during inference undermining the validity of the obtained results. We verify our detection criterion on a number of artificial and realistic misspecifications, ranging from toy conjugate models to complex models of decision making and disease outbreak dynamics applied to real data. Further, we show that posterior inference errors increase as a function of the distance between the true data-generating distribution and the typical set of simulations in the latent summary space. Thus, we demonstrate the dual utility of MMD as a method for detecting model misspecification and as a proxy for verifying the faithfulness of amortized Bayesian inference.
翻译:事实证明,在对不同研究领域进行高效模拟的贝耶斯罗框架的测算中,测深器在进行基于模拟的贝耶斯福框架的测深中表现得非常有力。 特别是,贝耶斯佛罗框架采用两步方法,在模拟程序暗含了概率函数定义的环境下进行摊销参数估计。 但是,在模拟对现实的描述不足时,这种测深器的准确性是多少? 在本文中,我们设想了模拟的推断中出现的模型误差类型,并系统地调查了贝耶斯福罗框架在这些误判中的表现。我们提议了一个扩大的优化目标,在潜在数据空间上设置一个概率结构,并利用最大平均值差异(MMD),以便在推断损害所获结果有效性的情形下,发现潜在的参数。 我们核实了我们关于一些人工和现实的误差的检测标准,从模拟模型到决策的复杂模型和对真实数据应用的疾病爆发动态。 此外,我们表明,事后推断误差是真实数据生成分布的距离和模拟模拟的典型模型之间的功能,我们展示了一种精确的模拟工具,用以核查精确的模拟空间模拟的效用。