BayesNDE：用于神经密度估计的贝叶斯生成建模
BayesNDE: Bayesian Generative Modeling for Neural Density Estimation
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中文总结 AI 辅助
BayesNDE提出基于贝叶斯生成建模的神经密度估计方法，无需可逆网络，通过样本特定后验构建自适应提议并利用桥接采样估计密度，在合成和真实数据集上提升了密度估计与异常检测性能。
中文摘要 AI 辅助
密度估计是统计学和机器学习中的一个基本问题。在这项工作中，我们引入了BayesNDE，一种基于贝叶斯生成建模的神经密度估计器。BayesNDE学习一个贝叶斯生成模型，并在无需可逆网络或雅可比行列式计算的情况下评估其密度。对于每个观测值，它推断一个样本特定的潜在后验，以构建一个自适应提议，将计算集中在对其密度贡献最大的区域。然后，桥接采样将该提议中的样本与单独的后验样本相结合，以估计密度。在非线性和多模态合成数据集上的实验表明，与最先进的神经密度估计器相比，密度值的估计和密度结构的恢复均有所改善。对真实数据集的应用进一步展示了改进的异常检测。这些结果共同凸显了BayesNDE作为一种灵活且有效的神经密度估计器，展示了后验推断如何将生成模型转化为密度估计工具。代码和教程可在以下网址获取：此https URL。
英文摘要
Density estimation is a fundamental problem in statistics and machine learning. In this work, we introduce BayesNDE, a neural density estimator based on Bayesian generative modeling. BayesNDE learns a Bayesian generative model and evaluates its density without requiring invertible networks or Jacobian-determinant computation. For each observation, it infers a sample-specific latent posterior to construct an adaptive proposal that focuses computation on regions contributing most to its density. Bridge sampling then combines samples from this proposal with separate posterior samples to estimate the density. Experiments on nonlinear and multimodal synthetic datasets show improved estimation of density values and better recovery of the density structure compared to the state-of-the-art neural density estimators. Applications to real-world datasets further demonstrate improved anomaly detection. Together, these results highlight BayesNDE as a flexible and effective neural density estimator, demonstrating how posterior inference can turn generative models into tools for density estimation. The code and tutorials are available at https://github.com/liuq-lab/BayesNDE.
发表机构
- Yale University（耶鲁大学）
机构由 AI 辅助整理，请以论文原文为准。
