In many areas of science, complex phenomena are modeled by stochastic parametric simulators, often featuring high-dimensional parameter spaces and intractable likelihoods. In this context, performing Bayesian inference can be challenging. In this work, we present a novel method that enables amortized inference over arbitrary subsets of the parameters, without resorting to numerical integration, which makes interpretation of the posterior more convenient. Our method is efficient and can be implemented with arbitrary neural network architectures. We demonstrate the applicability of the method on parameter inference of binary black hole systems from gravitational waves observations.

翻译：在许多科学领域,复杂的现象是由随机参数模拟器模拟的,往往以高维参数空间和难测的可能性为模型。在这方面,进行贝叶斯推论可能具有挑战性。在这项工作中,我们提出了一个新颖的方法,可以对参数的任意子集进行摊还性推论,而不必诉诸数字集成,这样更便于解释后背体。我们的方法是有效的,可以用任意的神经网络结构加以实施。我们展示了从引力波观测中二元黑洞系统的参数推论方法的适用性。