单项和多项研究中贝叶斯因子分析的快速变量推理

IF 1.4 2区数学 Q2 STATISTICS & PROBABILITY

Journal of Computational and Graphical Statistics Pub Date : 2024-05-15 DOI:10.1080/10618600.2024.2356173

Blake Hansen, Alejandra Avalos-Pacheco, Massimiliano Russo, Roberta De Vito

引用次数: 0

摘要

因素模型常用于分析单研究和多研究环境中的高维数据。这类模型的贝叶斯推断依赖于马尔可夫链蒙特卡罗（MCMC）方法...

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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Fast Variational Inference for Bayesian Factor Analysis in Single and Multi-Study Settings

Factors models are commonly used to analyze high-dimensional data in both single-study and multi-study settings. Bayesian inference for such models relies on Markov Chain Monte Carlo (MCMC) methods...

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来源期刊

Journal of Computational and Graphical Statistics 数学-统计学与概率论

CiteScore

3.50

自引率

8.30%

发文量

153

审稿时长

>12 weeks

期刊介绍： The Journal of Computational and Graphical Statistics (JCGS) presents the very latest techniques on improving and extending the use of computational and graphical methods in statistics and data analysis. Established in 1992, this journal contains cutting-edge research, data, surveys, and more on numerical graphical displays and methods, and perception. Articles are written for readers who have a strong background in statistics but are not necessarily experts in computing. Published in March, June, September, and December.