The field of astronomy is experiencing a data explosion driven by significant advances in observational instrumentation, and classical methods often fall short of addressing the complexity of modern astronomical datasets. Probabilistic graphical models offer powerful tools for uncovering the dependence structures and data-generating processes underlying a wide array of cosmic variables. By representing variables as nodes in a network, these models allow for the visualization and analysis of the intricate relationships that underpin theories of hierarchical structure formation within the universe. We highlight the value that graphical models bring to astronomical research by demonstrating their practical application to the study of exoplanets and host stars.

翻译：天文学领域正经历着由观测仪器重大进步驱动的数据爆炸，而经典方法往往难以应对现代天文数据集的复杂性。概率图模型为揭示众多宇宙变量背后的依赖结构和数据生成过程提供了强大工具。通过将变量表示为网络中的节点，这些模型能够可视化并分析支撑宇宙层级结构形成理论的复杂关系。我们通过展示图模型在系外行星与宿主恒星研究中的实际应用，阐明了这类模型为天文学研究带来的重要价值。