← 研踪 PaperTrailarXiv:2604.20568 · v3
Amortized Vine Copulas for High-Dimensional Density and Information Estimation
首次提交 2026-04-22 21:49
版本更新 2026-11-07 14:39
雷达观测 2026-11-10 11:14
摘要
Modeling high-dimensional dependencies while keeping likelihoods tractable re- mains challenging. Classical vine copulas provide an explicit dependence structure but require repeated, costly bivariate fitting. We propose Vine Denoising Copula (VDC)1, an amortized estimator for simplified vines on continuous data. VDC trains a single bivariate denoising network on simulated copulas and reuses it across all vine edges. For each edge, the network maps an empirical histogram to a density grid, and iterative proportional fitting approximately enforces uniform grid marginals. The predicted densities and cached conditional transforms support stan- dard vine recursion, likelihood evaluation, sampling, and information estimation. This retains the classical vine factorization while replacing repeated per-edge fitting with reusable neural inference. Across synthetic and real-data benchmarks, VDC combines accurate bivariate density estimation, competitive information estimates, and faster high-dimensional vine fitting after pretraining. Performance varies across datasets; finite grid resolution and conditional prediction remain limitations. The explicit fitted structure supports edge- and tree-level information decomposition, making repeated dependence analysis more practical.
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v3 · 2026-11-07 14:39 · 最新观测版本
Amortized Vine Copulas for High-Dimensional Density and Information Estimation
Modeling high-dimensional dependencies while keeping likelihoods tractable re- mains challenging. Classical vine copulas provide an explicit dependence structure but require repeated, costly bivariate fitting. We propose Vine Denoising Copula (VDC)1, an amortized estimator for simplified vines on continuous data. VDC trains a single bivariate denoising network on simulated copulas and reuses it across all vine edges. For each edge, the network maps an empirical histogram to a density grid, and iterative proportional fitting approximately enforces uniform grid marginals. The predicted densities and cached conditional transforms support stan- dard vine recursion, likelihood evaluation, sampling, and information estimation. This retains the classical vine factorization while replacing repeated per-edge fitting with reusable neural inference. Across synthetic and real-data benchmarks, VDC combines accurate bivariate density estimation, competitive information estimates, and faster high-dimensional vine fitting after pretraining. Performance varies across datasets; finite grid resolution and conditional prediction remain limitations. The explicit fitted structure supports edge- and tree-level information decomposition, making repeated dependence analysis more practical.
查看此版本解读 →查看此版本原文 ↗