Conditional Independence (CI) graphs are a type of probabilistic graphical models that are primarily used to gain insights about feature relationships. Each edge represents the partial correlation between the connected features which gives information about their direct dependence. In this survey, we list out different methods and study the advances in techniques developed to recover CI graphs. We cover traditional optimization methods as well as recently developed deep learning architectures along with their recommended implementations. To facilitate wider adoption, we include preliminaries that consolidate associated operations, for example techniques to obtain covariance matrix for mixed datatypes.

翻译：条件独立图是一类概率图模型，主要用于揭示特征间的关系。图中每条边表示连接特征之间的偏相关系数，反映其直接依赖性。本综述列举了恢复条件独立图的不同方法，并梳理相关技术进展。涵盖传统优化方法及近期开发的深度学习架构，同时给出其推荐实现方式。为促进更广泛应用，本文纳入整合相关操作的基础知识，例如获取混合数据类型协方差矩阵的技术。