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English(EN) Multivariate Varying-Coefficient BART with Graphical Horseshoe Priors

新的multiVCBART框架通过灵活的系数建模增强了多元回归

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了multiVCBART，一种用于多元回归的新颖框架，该框架联合建模结果特定的系数曲面和稀疏的残差精度矩阵。这种方法允许预测变量效应随着不同结果的修饰符非线性地变化，同时还通过图形马蹄先验捕获简约的残差条件依赖性。该论文介绍了一种用于计算的高效采样器，并从理论上确立了此类模型的后验收缩率，证明了其适应底层平滑度和结构稀疏性的能力。实证结果表明，multiVCBART在稀疏、高维数据集上的表现优于现有的多元树模型和贝叶斯SUR竞争对手。 AI

影响 这一新的统计框架可以提高AI研究中复杂多元分析的准确性和可解释性。

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。

在 arXiv stat.ML 阅读 →

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新的multiVCBART框架通过灵活的系数建模增强了多元回归

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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Soham Ghosh, Sameer K. Deshpande ·

    多元变系数BART与图示马蹄先验

    arXiv:2606.29114v1 Announce Type: cross Abstract: Modern multivariate regression problems involve several related outcomes whose regression effects are not only nonlinear, heterogeneous, and outcome-specific, but also where the residual dependence among outcomes is scientifically…

  2. arXiv stat.ML TIER_1 English(EN) · Sameer K. Deshpande ·

    具有图示马蹄先验的多变量变系数BART模型

    Modern multivariate regression problems involve several related outcomes whose regression effects are not only nonlinear, heterogeneous, and outcome-specific, but also where the residual dependence among outcomes is scientifically meaningful. Existing multivariate Bayesian tree-b…

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