Markov chain Monte Carlo for dynamic generalised linear models
Markov chain Monte Carlo for dynamic generalised linear models
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DOI：
10.1093/biomet/85.1.215
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发表时间：
1998-03-01
期刊：
影响因子：
2.7
通讯作者：
Gamerman, D
中科院分区：
文献类型：
--
作者：
Gamerman, D
本文提出了一种新的指数族观测动态模型的贝叶斯推断方法。该方法是;模拟为基础，并涉及使用马尔可夫链蒙特卡罗技术。一个Metropolis-Hastings算法与吉布斯采样器相结合，在重复使用的正常动态线性模型的调整版本。不同的替代方案的基础上采样的系统扰动和状态参数单独和在一个块推导和比较。该方法在获取具有状态参数和未知超参数的后验样本时是完全贝叶斯的。给出了具有稀疏计数和缺失值的真实的数据集的插图。概述了为适应更一般的观测和干扰演化形式和分布而进行的扩展。
This paper presents a new methodological approach for carrying out Bayesian inference about dynamic models for exponential-family observations. The approach is; simulation-based and involves the use of Markov chain Monte Carlo techniques. A Metropolis-Hastings algorithm is combined with the Gibbs sampler in repeated use of an adjusted version of normal dynamic linear models. Different alternative schemes based on sampling from the system disturbances and state parameters separately and in a block are derived and compared. The approach is fully Bayesian in obtaining posterior samples with state parameters and unknown hyperparameters. Illustrations with real datasets with sparse counts and missing values are presented. Extensions to accommodate more general evolution forms and distributions for observations and disturbances are outlined.