Abstract
<jats:title>Abstract</jats:title> <jats:p>Sparse graphical modeling plays a crucial role in high‐dimensional data analysis by capturing conditional independence structures among variables. This article provides a concise overview of methods for learning sparse graphical models, covering both Gaussian and non‐Gaussian data, as well as methods for the joint estimation of multiple graphical models. Key methodologies are discussed, including nodewise regression, graphical Lasso, and conditional independence test‐based methods. In particular, conditional independence test‐based methods offer theoretical flexibility and broad applicability across various data scenarios, including Gaussian and non‐Gaussian data, datasets with and without missing values, datasets collected under single or multiple conditions, and homologous and heterogeneous model structures. Additionally, this article explores the role of sparse graphical models in high‐dimensional inference, where they improve both model interpretability and statistical efficiency by leveraging the learned conditional independence structure of explanatory variables.</jats:p>