We present an alternative to reweighting techniques for modifying distributions to account for a desired change in an underlying conditional distribution, as is often needed to correct for mis-modelling in a simulated sample. We employ conditional normalizing flows to learn the full conditional probability distribution from which we sample new events for conditional values drawn from the target distribution to produce the desired, altered distribution. In contrast to common reweighting techniques, this procedure is independent of binning choice and does not rely on an estimate of the density ratio between two distributions. In several toy examples we show that normalizing flows outperform reweighting approaches to match the distribution of the target.We demonstrate that the corrected distribution closes well with the ground truth, and a statistical uncertainty on the training dataset can be ascertained with bootstrapping. In our examples, this leads to a statistical precision up to three times greater than using reweighting techniques with identical sample sizes for the source and target distributions. We also explore an application in the context of high energy particle physics.

翻译：我们提出了一种替代加权技术的方法，用于修正分布以应对潜在条件分布的期望变化，这在模拟样本中常需修正建模误差。我们采用条件归一化流学习完整的条件概率分布，从中为来自目标分布的条件值采样新事件，从而生成所需的修正分布。与常见加权技术不同，该方法不依赖分箱选择，也无需估计两个分布之间的密度比。在多个玩具示例中，我们展示了归一化流在匹配目标分布方面优于加权方法。我们证明修正分布与真实分布拟合良好，且可通过自举法确定训练数据集的统计不确定性。在示例中，该方法在源分布与目标分布样本量相同时，统计精度比加权技术高出三倍。我们还探讨了该方案在高能粒子物理领域中的应用。