无向高斯图形模型中的贝叶斯结构学习：文献综述与实证比较

IF 3 1区数学 Q1 STATISTICS & PROBABILITY

Journal of the American Statistical Association Pub Date : 2024-08-30 DOI:10.1080/01621459.2024.2395504

Lucas Vogels, Reza Mohammadi, Marit Schoonhoven, Ş. İlker Birbil

引用次数: 0

摘要

高斯图形模型为揭示多变量之间的条件依赖结构提供了一个强大的框架。揭示条件依赖网络的过程是...

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Bayesian Structure Learning in Undirected Gaussian Graphical Models: Literature Review with Empirical Comparison

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...

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来源期刊

Journal of the American Statistical Association 数学-统计学与概率论

CiteScore

7.50

自引率

8.10%

发文量

168

审稿时长

12 months

期刊介绍： Established in 1888 and published quarterly in March, June, September, and December, the Journal of the American Statistical Association ( JASA ) has long been considered the premier journal of statistical science. Articles focus on statistical applications, theory, and methods in economic, social, physical, engineering, and health sciences. Important books contributing to statistical advancement are reviewed in JASA . JASA is indexed in Current Index to Statistics and MathSci Online and reviewed in Mathematical Reviews. JASA is abstracted by Access Company and is indexed and abstracted in the SRM Database of Social Research Methodology.