Partially Linear Additive Gaussian Graphical Models

An additive graphical model for discrete data

We introduce a nonparametric graphical model for discrete node variables based on additive conditional independence. Additive conditional independence is a three way statistical relation that shares similar properties with conditional independence by satisfying the semi-graphoid axioms. Based on this relation we build an additive graphical model for discrete variables that does not suffer from the restriction of a parametric model such as the Ising model. We develop an estimator of the new graphical model via the penalized estimation of the discrete version of the additive precision operator a…

基于分数的结构先验的部分已知高斯图模型估计

Estimation of partially known Gaussian graphical models with score-based structural priors

本文提出一种融合图上先验信息的部分已知高斯图模型支持估计算法，利用退火朗之万扩散和图神经网络估计分数函数，从后验分布采样以提升估计性能。

Sparse Partially Linear Additive Models

The generalized partially linear additive model (GPLAM) is a flexible and interpretable approach to building predictive models. It combines features in an additive manner, allowing each to have either a linear or nonlinear effect on the response. However, the choice of which features to treat as linear or nonlinear is typically assumed known. Thus, to make a GPLAM a viable approach in situations in which little is known $a~priori$ about the features, one must overcome two primary model selection challenges: deciding which features to include in the model and determining which of these features…

Practical Algorithms for Orientations of Partially Directed Graphical Models

In observational studies, the true causal model is typically unknown and needs to be estimated from available observational and limited experimental data. In such cases, the learned causal model is commonly represented as a partially directed acyclic graph (PDAG), which contains both directed and undirected edges indicating uncertainty of causal relations between random variables. The main focus of this paper is on the maximal orientation task, which, for a given PDAG, aims to orient the undirected edges maximally such that the resulting graph represents the same Markov equivalent DAGs as the …

基于部分可解释神经网络的美国极端野火时空回归建模

Regression modelling of spatiotemporal extreme U.S. wildfires via partially-interpretable neural networks

针对美国极端野火风险量化需求，提出结合线性/加性回归与深度学习的部分可解释神经网络及新型极值点过程模型，在高维野火数据上预测性能显著优于传统回归方法。

关于部分观测线性因果模型的参数可识别性研究

On the Parameter Identifiability of Partially Observed Linear Causal Models

本文针对部分观测线性因果模型，在更一般的设置下（允许所有变量灵活关联、考虑所有边系数），识别参数的三种不确定性类型，提出充分且部分必要的图形条件，设计基于似然的估计方法并通过实验验证其有效性。