Estimation of Graphical Models: An Overview of Selected Topics
本文回顾了图模型的基本概念，重点介绍估计方法和计算算法，并探讨复杂图结构和噪声数据的处理，对回归和分类应用有参考价值。
Summary Graphical modelling is an important branch of statistics that has been successfully applied in biology, social science, causal inference and so on. Graphical models illuminate connections between many variables and can even describe complex data structures or noisy data. Graphical models have been combined with supervised learning techniques such as regression modelling and classification analysis with multi‐class responses. This paper first reviews some fundamental graphical modelling concepts, focusing on estimation methods and computational algorithms. Several advanced topics are then considered, delving into complex graphical structures and noisy data. Applications in regression and classification are considered throughout.