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English(EN) Likelihood-free inference with nuisance parameters through normalizing flows

新统计方法使用归一化流进行无似然推断

作者 PulseAugur 编辑部 · [1 个来源] ·

已开发出一种新的无似然推断统计方法，在处理干扰参数时特别有用。该方法利用基于神经网络的归一化流来识别一个枢轴统计量，该统计量显示其p值的平均Kullback–Leibler散度最小。与Welch检验和似然比剖面法等现有技术相比，该方法在小样本量上，在功效和速度方面表现更优，并且可以纳入不变性的先验知识。 AI

排序理由 该集群包含一篇详细介绍新统计方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv stat.ML 阅读 →

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新统计方法使用归一化流进行无似然推断

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该集群包含一篇详细介绍新统计方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Phil Assheton ·

    通过归一化流处理带有扰动参数的无似然推断

    arXiv:2609.10534v1 Announce Type: cross Abstract: We present a simple decomposition of a neural-network-based normalizing flow that naturally uncovers a pivotal statistic (or something close) in the presence of nuisance parameters, based only on a sample generator from the distri…

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