图形模型的估计：选题概述

IF 1.7 3区数学 Q1 STATISTICS & PROBABILITY

International Statistical Review Pub Date : 2023-10-04 DOI:10.1111/insr.12552

Li-Pang Chen

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引用次数: 0

摘要

图形建模是统计学的一个重要分支，已成功应用于生物学、社会科学、因果推理等领域。图形模型可以阐明许多变量之间的联系，甚至可以描述复杂的数据结构或噪声数据。图形模型已与监督学习技术（如回归建模和多类响应分类分析）相结合。本文首先回顾了一些基本的图形建模概念，重点是估计方法和计算算法。然后，探讨了几个高级主题，深入研究了复杂的图形结构和噪声数据。全文还考虑了回归和分类中的应用。

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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Estimation of Graphical Models: An Overview of Selected Topics

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.

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来源期刊

International Statistical Review 数学-统计学与概率论

CiteScore

4.30

自引率

5.00%

发文量

审稿时长

>12 weeks

期刊介绍： International Statistical Review is the flagship journal of the International Statistical Institute (ISI) and of its family of Associations. It publishes papers of broad and general interest in statistics and probability. The term Review is to be interpreted broadly. The types of papers that are suitable for publication include (but are not limited to) the following: reviews/surveys of significant developments in theory, methodology, statistical computing and graphics, statistical education, and application areas; tutorials on important topics; expository papers on emerging areas of research or application; papers describing new developments and/or challenges in relevant areas; papers addressing foundational issues; papers on the history of statistics and probability; white papers on topics of importance to the profession or society; and historical assessment of seminal papers in the field and their impact.