基于归一化流的稀有事件概率学习
Rare Event Probability Learning by Normalizing Flows
AI总结：
本文提出NOFIS方法，利用归一化流学习嵌套提议分布并结合重要性采样，高效估计稀有事件概率，在10个测试用例上优于基线方法。
摘要：
稀有事件被定义为发生概率极低的事件。准确估计此类微小概率在众多领域中至关重要。传统蒙特卡洛方法效率低下，需要大量样本才能获得可靠估计。受归一化流精确采样能力的启发，我们重新审视这一挑战，并提出归一化流辅助重要性采样方法，称为NOFIS。NOFIS首先通过最小化KL散度损失，学习与预定义的嵌套子集事件相关联的一系列提议分布。随后，它利用重要性采样并结合最后一个提议分布来估计稀有事件概率。我们通过全面的定性可视化验证了NOFIS方法的有效性，证实了所学提议分布的最优性，同时一系列涵盖10个不同测试用例的定量实验凸显了NOFIS相对于基线方法的优越性。
英文摘要：
A rare event is defined by a low probability of occurrence. Accurate estimation of such small probabilities is of utmost importance across diverse domains. Conventional Monte Carlo methods are inefficient, demanding an exorbitant number of samples to achieve reliable estimates. Inspired by the exact sampling capabilities of normalizing flows, we revisit this challenge and propose normalizing flow assisted importance sampling, termed NOFIS. NOFIS first learns a sequence of proposal distributions associated with predefined nested subset events by minimizing KL divergence losses. Next, it estimates the rare event probability by utilizing importance sampling in conjunction with the last proposal. The efficacy of our NOFIS method is substantiated through comprehensive qualitative visualizations, affirming the optimality of the learned proposal distribution, as well as a series of quantitative experiments encompassing $10$ distinct test cases, which highlight NOFIS's superiority over baseline approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
