论文标题
强大的贝叶斯推断通过MMD后引导程序针对基于模拟器的模型
Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap
论文作者
论文摘要
基于模拟器的模型是可能性棘手的模型，但可以模拟合成数据。它们通常用于描述复杂的现实现象，因此通常可以在实践中误指出。不幸的是，在这些情况下，现有的模拟器方法的性能很差。在本文中，我们提出了一种基于后引导程序和最大平均差异估计器的新算法。这导致具有强大鲁棒性特性的高度可行的贝叶斯推理算法。这是通过深入的理论研究来证明的，该研究包括泛化界限和后验频繁的一致性和鲁棒性的证明。然后在包括G-and-K分布和切换切换模型在内的一系列示例中评估该方法。
Simulator-based models are models for which the likelihood is intractable but simulation of synthetic data is possible. They are often used to describe complex real-world phenomena, and as such can often be misspecified in practice. Unfortunately, existing Bayesian approaches for simulators are known to perform poorly in those cases. In this paper, we propose a novel algorithm based on the posterior bootstrap and maximum mean discrepancy estimators. This leads to a highly-parallelisable Bayesian inference algorithm with strong robustness properties. This is demonstrated through an in-depth theoretical study which includes generalisation bounds and proofs of frequentist consistency and robustness of our posterior. The approach is then assessed on a range of examples including a g-and-k distribution and a toggle-switch model.
