In this work, we demonstrate the Empirical Bayes approach to learning a
Dynamic Bayesian Network. By starting with several point estimates of structure
and weights, we can use a data-driven prior to subsequently obtain a model to
quantify uncertainty. This approach uses a recent development of Generalized
Variational Inference, and indicates the potential of sampling the uncertainty
of a mixture of DAG structures as well as a parameter posterior.

通过经验贝叶斯方法学习动态贝叶斯网络的研究，使用数据驱动的先验获得模型以量化不确定性，结合广义变分推断的最新发展，表明了对 DAG 结构和参数后验的不确定性的采样的潜力。

使用广义变分推断的动态贝叶斯网络的经验贝叶斯

Empirical Bayes for Dynamic Bayesian Networks Using Generalized  Variational Inference

Casually captured Neural Radiance Fields (NeRFs) suffer from artifacts such
as floaters or flawed geometry when rendered outside the camera trajectory.
Existing evaluation protocols often do not capture these effects, since they
usually only assess image quality at every 8th frame of the training capture.
To push forward progress in novel-view synthesis, we propose a new dataset and
evaluation procedure, where two camera trajectories are recorded of the scene:
one used for training, and the other for evaluation. In this more challenging
in-the-wild setting, we find that existing hand-crafted regularizers do not
remove floaters nor improve scene geometry. Thus, we propose a 3D
diffusion-based method that leverages local 3D priors and a novel density-based
score distillation sampling loss to discourage artifacts during NeRF
optimization. We show that this data-driven prior removes floaters and improves
scene geometry for casual captures.

该研究提出了一种新的数据集和评估程序，利用 3D 扩散方法和基于密度的分数蒸馏采样损失来避免 NeRF 优化过程中的呈现伪像和错误几何图形问题。使用这种数据驱动的先验方法可以去除浮动物并改善场景几何结构。