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DireSMC：用序贯蒙特卡洛引导扩散模型采样稀有事件

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2026年10月7日，arXiv Statistics Machine Learning 发布论文，提出 Diffusion Importance Sampling of Rare Events（DireSMC）。该方法是一种序贯蒙特卡洛方案，用于引导加权样本群体趋向稀有事件，同时给出校准后的概率估计。目前公开信息仅涉及该论文内容，未见后续实验验证或第三方复现报道。

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LatestOct 7
arXiv 发布 DireSMC 论文，提出用序贯蒙特卡洛引导扩散模型采样稀有事件并给出校准概率估计。

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Oct 7, 2026
  1. arXiv · Statistics Machine Learning
    用序贯蒙特卡洛引导扩散模型捕捉稀有事件

    论文提出 Diffusion Importance Sampling of Rare Events（DireSMC），一种序贯蒙特卡洛方案，引导加权样本群体趋向稀有事件，同时给出校准后的概率估计。

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