Parametric stochastic simulators are ubiquitous in science, often featuring high-dimensional input parameters and/or an intractable likelihood. Performing Bayesian parameter inference in this context can be challenging. We present a neural simulation-based inference algorithm which simultaneously offers simulation efficiency and fast empirical posterior testability, which is unique among modern algorithms. Our approach is simulation efficient by simultaneously estimating low-dimensional marginal posteriors instead of the joint posterior and by proposing simulations targeted to an observation of interest via a prior suitably truncated by an indicator function. Furthermore, by estimating a locally amortized posterior our algorithm enables efficient empirical tests of the robustness of the inference results. Since scientists cannot access the ground truth, these tests are necessary for trusting inference in real-world applications. We perform experiments on a marginalized version of the simulation-based inference benchmark and two complex and narrow posteriors, highlighting the simulator efficiency of our algorithm as well as the quality of the estimated marginal posteriors.
翻译:在科学中,常有高维输入参数和/或难测的可能性。在这方面,进行巴耶斯参数的推论可能具有挑战性。我们提出了一个神经模拟推论算法,同时提供模拟效率和快速经验远地点测试,这在现代算法中是独一无二的。我们的方法是模拟效率,方法是同时估计低维边际边远地点,而不是联合后继物,并提议模拟,目的是通过一个指标函数事先适当抽出的兴趣观测。此外,通过估算一个本地摊销的远地点,我们的算法能够有效地对推论结果的稳健性进行实证测试。由于科学家无法了解地面真相,这些测试对于相信真实世界应用中的推论是有必要的。我们对一个边际的模拟边际边远地点基准和两个复杂而狭窄的远地点进行了实验,突出我们的算法的模拟效率以及估计边缘后继物的质量。