English(EN) Rotated Mean-Field Variational Inference and Iterative Gaussianization

新方法使用迭代高斯化改进密度采样

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-10 04:00

研究人员引入了一种新颖的迭代高斯化方法，用于从非归一化密度中采样。该技术在旋转坐标系内重复应用平均场变分推断 (MFVI) 来逼近标准高斯分布。通过基于主成分分析 (PCA) 的方法选择信息性旋转，可以提高该方法的效率，从而在贝叶斯后验采样任务上显著优于标准的 MFVI。 AI

排序理由该集群包含一篇详细介绍新颖计算方法的新学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

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arXiv stat.ML TIER_1 English(EN) · Yifan Chen, Sifan Liu · 2026-07-10 04:00

旋转均场变分推断与迭代高斯化

arXiv:2510.07732v2 Announce Type: replace-cross Abstract: We propose an iterative Gaussianization method for sampling from unnormalized densities by repeatedly applying mean-field variational inference (MFVI) in rotated coordinate systems. At each iteration, the method selects a …