用多级模拟提升昂贵模型的贝叶斯推断精度。
Multilevel neural simulation-based inference
- 结合多级蒙特卡洛，利用不同成本和精度的模拟器
- 相同计算预算下，推断误差显著降低
- 适合高成本模拟器场景，如物理建模与工程仿真
神经模拟基推断（Neural SBI）是一类在无法显式写出似然函数时进行贝叶斯推断的流行方法，广泛应用于科学与工程领域。然而，当模拟器计算成本高昂时，可执行的模拟次数受限，导致推断性能下降。本文提出一种新方法，利用多级蒙特卡洛技术，在存在多个成本与精度各异的模拟器时，显著提升神经SBI的准确性。通过理论分析与大量实验验证，该方法在固定计算预算下能有效降低推断误差。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。