使用WinBUGS的贝叶斯函数数据分析
Bayesian Functional Data Analysis Using WinBUGS.
作者信息
Crainiceanu Ciprian M, Goldsmith A Jeffrey
机构信息
Department of Biostatistics, Johns Hopkins University, 615 N. Wolfe St. E3636, Baltimore, MD 21205, United States of America, URL: http://www.biostat.jhsph.edu/~ccrainic/
出版信息
J Stat Softw. 2010 Jan 1;32(11).
Abstract
We provide user friendly software for Bayesian analysis of functional data models using WinBUGS 1.4. The excellent properties of Bayesian analysis in this context are due to: (1) dimensionality reduction, which leads to low dimensional projection bases; (2) mixed model representation of functional models, which provides a modular approach to model extension; and (3) orthogonality of the principal component bases, which contributes to excellent chain convergence and mixing properties. Our paper provides one more, essential, reason for using Bayesian analysis for functional models: the existence of software.
摘要
我们提供了用户友好型软件，用于使用WinBUGS 1.4对函数数据模型进行贝叶斯分析。在此背景下，贝叶斯分析的卓越特性归因于：（1）降维，这会产生低维投影基；（2）函数模型的混合模型表示，它提供了一种模块化的模型扩展方法；以及（3）主成分基的正交性，这有助于实现出色的链收敛和混合特性。我们的论文给出了使用贝叶斯分析处理函数模型的另一个重要原因：软件的存在。
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