Title: Multilevel neural simulation-based inference Show Chinese title

Title: 多级神经网络仿真基础推断

Abstract: Neural simulation-based inference (SBI) is a popular set of methods for Bayesian inference when models are only available in the form of a simulator. These methods are widely used in the sciences and engineering, where writing down a likelihood can be significantly more challenging than constructing a simulator. However, the performance of neural SBI can suffer when simulators are computationally expensive, thereby limiting the number of simulations that can be performed. In this paper, we propose a novel approach to neural SBI which leverages multilevel Monte Carlo techniques for settings where several simulators of varying cost and fidelity are available. We demonstrate through both theoretical analysis and extensive experiments that our method can significantly enhance the accuracy of SBI methods given a fixed computational budget. Show Chinese abstract

Abstract: 基于神经网络的模拟推断（SBI）是一组在模型仅以模拟器形式可用时用于贝叶斯推断的流行方法。这些方法在科学和工程领域被广泛使用，在这些领域中，写出似然函数可能比构建模拟器要困难得多。然而，当模拟器计算成本高昂时，神经SBI的性能可能会受到影响，从而限制了可以执行的模拟次数。在本文中，我们提出了一种新的神经SBI方法，该方法利用多级蒙特卡洛技术，适用于存在多个不同成本和精度的模拟器的场景。我们通过理论分析和大量实验表明，我们的方法可以在固定的计算预算下显著提高SBI方法的准确性。