HIERARCHICAL BAYESIAN-ANALYSIS OF CHANGEPOINT PROBLEMS
HIERARCHICAL BAYESIAN-ANALYSIS OF CHANGEPOINT PROBLEMS
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DOI：
10.2307/2347570
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发表时间：
1992-01-01
影响因子：
1.6
通讯作者：
SMITH, AFM
中科院分区：
文献类型：
--
作者：
CARLIN, BP;GELFAND, AE;SMITH, AFM
提出了一种层次贝叶斯变点模型的通用方法。特别是，期望的边际后验密度是利用吉布斯采样器，一种迭代蒙特卡罗方法。这种方法避免了复杂的解析和数值高维积分过程。我们包括了变化回归、变化泊松过程和变化马尔可夫链的应用。在这些上下文中，我们处理了几个以前无法解决的问题。
A general approach to hierarchical Bayes changepoint models is presented. In particular, desired marginal posterior densities are obtained utilizing the Gibbs sampler, an iterative Monte Carlo method. This approach avoids sophisticated analytic and numerical high dimensional integration procedures. We include an application to changing regressions, changing Poisson processes and changing Markov chains. Within these contexts we handle several previously inaccessible problems.