无向高斯图模型中的贝叶斯结构学习：文献综述与实证比较
Bayesian Structure Learning in Undirected Gaussian Graphical Models: Literature Review with Empirical Comparison
AI总结：
本文综述并实证比较了无向高斯图模型中用于结构学习的多种贝叶斯方法，通过模拟研究评估其效能并展示真实数据集应用，为各层级研究者提供全面参考。
摘要：
高斯图模型提供了一个强大的框架，用于揭示多元变量间的条件依赖结构。揭示条件依赖网络的过程被称为结构学习。贝叶斯方法能够衡量条件关系的不确定性并纳入先验信息。然而，由于贝叶斯方法的计算负担，频率派方法往往更受青睐。在过去十年中，贝叶斯方法取得了实质性进展，一些方法现在能够在短短几分钟内生成多达一千个变量的图的准确估计。尽管取得了这些进展，但尚未对所有近期方法进行全面综述或实证比较。本文深入探讨了用于结构学习的各种贝叶斯方法，并通过全面的模拟研究评估了它们的效能。我们还演示了如何将贝叶斯结构学习应用于真实数据集，并为未来的研究提供了方向。本研究为新手、从业者和专家提供了对该动态领域的详尽概述。
英文摘要：
Gaussian graphical models provide a powerful framework to reveal the conditional dependency structure between multivariate variables. The process of uncovering the conditional dependency network is known as structure learning. Bayesian methods can measure the uncertainty of conditional relationships and include prior information. However, frequentist methods are often preferred due to the computational burden of the Bayesian approach. Over the last decade, Bayesian methods have seen substantial improvements, with some now capable of generating accurate estimates of graphs up to a thousand variables in mere minutes. Despite these advancements, a comprehensive review or empirical comparison of all recent methods has not been conducted. This paper delves into a wide spectrum of Bayesian approaches used for structure learning and evaluates their efficacy through a comprehensive simulation study. We also demonstrate how to apply Bayesian structure learning to a real-world data set and provide directions for future research. This study gives an exhaustive overview of this dynamic field for newcomers, practitioners, and experts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
