基于变分贝叶斯的屏蔽数据分析

IF 3.7 3区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Himanshu Rai , Sanjeev K. Tomer
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引用次数: 0

摘要

贝叶斯竞争风险分析往往导致难以处理的后验,为此,马尔可夫链蒙特卡罗(MCMC)方法经常用于评估各种感兴趣的估计量。然而,当同时分析多个风险时,MCMC方法可能会消耗大量的计算时间。本文介绍了变分贝叶斯,一种机器学习技术,作为MCMC的有效替代,用于贝叶斯分析竞争风险数据。变分贝叶斯证明了比MCMC更快的收敛速度,特别是在广泛竞争的风险数据集的背景下。我们通过模拟研究比较了变分贝叶斯在吉布斯抽样中的表现,考虑了风险的数量。此外,我们将这两种方法应用于计算机硬盘驱动器的实际数据集分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Variational Bayes for analysis of masked data
Bayesian competing risks analysis in presence of masked data often leads to an intractable posterior, for which Markov chain Monte Carlo (MCMC) methods are frequently utilized to evaluate various estimators of interest. However, while analyzing several risks simultaneously, MCMC methods may consume substantial amount of computation time. This paper introduces Variational Bayes, a machine learning technique, as an efficient alternative to MCMC for Bayesian analysis of competing risk data. Variational Bayes demonstrates faster convergence than MCMC, particularly in the context of extensive competing risk datasets. We compare the performance of variational Bayes over Gibbs sampling with respect to the number of considered risks through a simulation study. Additionally, we apply the two methods to analyze a real data set of computer hard drives.
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来源期刊
Journal of Computational Science
Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
5.50
自引率
3.00%
发文量
227
审稿时长
41 days
期刊介绍: Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).
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