有向无环图的有限混合模型拟合及其在教育传播中的应用

无圈有向混合图对二值数据的极大似然拟合

Maximum likelihood fitting of acyclic directed mixed graphs to binary data

无环有向混合图，也称为半马尔可夫模型，表示由具有潜在变量的DAG模型在观察到的边缘上诱导的条件独立结构。在本文中，我们提出了第一种方法，这些模型拟合二进制数据使用极大似然估计。

联合干预和观察数据：有向无圈图的干预马尔可夫等价类的估计

Jointly interventional and observational data: estimation of interventional Markov equivalence classes of directed acyclic graphs

在许多应用程序中，我们有观察和（随机）干预数据。我们提出了一个高斯似然框架联合建模的不同的数据类型，基于全球参数组成的有向无圈图（DAG）和相应的边缘权重和误差方差。由于参数的全局性质，极大似然估计是合理的，每个干预只有一个或几个数据点。我们证明了BIC标准的一致性，用于估计干预性的马尔可夫等价类的DAG，这是小于的观测模拟，由于增加部分可识别性的干预数据。这种改进的可识别性直接影响到更严格的界限推断因果关系的影响。除了方法和理论推导，我们提出了实证结果，从真实和模拟数据。

从巴西手语中识别静态符号：比较大间隔决策导向非循环图、投票支持向量机和人工神经网络

Recognizing Static Signs from the Brazilian Sign Language: Comparing Large-Margin Decision Directed Acyclic Graphs, Voting Support Vector Machines and Artificial Neural Networks

在本文中，我们探讨和详细介绍了我们的实验在一个高维，多类图像分类的问题，经常发现在自动识别的手语。在这里，我们的努力是针对比较的特点，优点和缺点的创建和训练支持向量机部署在有向无环图和人工神经网络分类标志从巴西手语（LIBRAS）。我们探讨了不同的分类器，超参数和多类决策方案如何影响每个分类器的性能，效率和易用性。我们提供了捕捉准确性和效率的超参数表面图，DDAG和1-vs-1 SVM之间的比较，以及使用弹性反向传播训练ANN时的启发式效果。我们报告统计学显着的结果，使用科恩的Kappa统计列联表。

Learning Discrete Directed Acyclic Graphs via Backpropagation

Recently continuous relaxations have been proposed in order to learn Directed Acyclic Graphs (DAGs) from data by backpropagation, instead of using combinatorial optimization. However, a number of techniques for fully discrete backpropagation could instead be applied. In this paper, we explore that direction and propose DAG-DB, a framework for learning DAGs by Discrete Backpropagation. Based on the architecture of Implicit Maximum Likelihood Estimation [I-MLE, arXiv:2106.01798], DAG-DB adopts a probabilistic approach to the problem, sampling binary adjacency matrices from an implicit probabilit…

通过结构化、监督和生成对抗学习测试有向无环图

Testing Directed Acyclic Graph via Structural, Supervised and Generative Adversarial Learning

本文提出一种基于神经网络的DAG假设检验方法，允许非线性关联和时间相关数据，并提供了渐近保证，通过模拟和大脑连接网络分析验证了有效性。

Inference for a Large Directed Acyclic Graph with Unspecified Interventions

Statistical inference of directed relations given some unspecified interventions (i.e., the intervention targets are unknown) is challenging. In this article, we test hypothesized directed relations with unspecified interventions. First, we derive conditions to yield an identifiable model. Unlike classical inference, testing directed relations requires identifying the ancestors and relevant interventions of hypothesis-specific primary variables. To this end, we propose a peeling algorithm based on nodewise regressions to establish a topological order of primary variables. Moreover, we prove th…