Non-iterative sampling-based Bayesian methods for identifying changepoints in the sequence of cases of haemolytic uraemic syndrome.
Non-iterative sampling-based Bayesian methods for identifying changepoints in the sequence of cases of haemolytic uraemic syndrome.
复制标题
基于非迭代采样的贝叶斯方法，用于识别溶血性尿毒综合征病例序列中的变化点。
DOI：
10.1016/j.csda.2009.02.006
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发表时间：
2009
影响因子：
1.8
通讯作者：
Tan,Ming
中科院分区：
文献类型：
--
作者：
Tian,Guo-Liang;Ng,KaiWang;Li,Kai-Can;Tan,Ming
腹泻相关溶血性尿毒症综合征(HUS)是一种影响肾脏和其他器官的疾病。基于1970年至1989年分别在英国伯明翰和纽卡斯尔收集的每年HUS病例的数量，我们考虑了贝叶斯变点分析，特别关注Poisson变点模型。对于变点个数未知的变点模型，我们提出了一种新的非迭代贝叶斯抽样方法(称为精确IBF抽样)，它完全避免了迭代马尔可夫链蒙特卡罗(MCMC)方法的收敛和收敛速度慢的问题。其思想是首先利用抽样逆贝叶斯公式(IBF)推导出给定观测数据的潜在数据的条件分布，然后从完全数据的后验分布中提取IID样本。为了选择合适的模型(或确定变点的个数)，我们发展了两个公式来精确计算边际似然(或贝叶斯因子)，分别使用精确的IBF输出和逐点IBF。使用所提出的方法对HUS数据进行了重新分析。仿真结果验证了所提方法的有效性。
Diarrhoea-associated Haemolytic Uraemic syndrome (HUS) is a disease that affects the kidneys and other organs. Motivated by the annual number of cases of HUS collected in Birmingham and Newcastle of England, respectively, from 1970 to 1989, we consider Bayesian changepoint analysis with specific attention to Poisson changepoint models. For changepoint models with unknown number of changepoints, we propose a new non-iterative Bayesian sampling approach (called exact IBF sampling), which completely avoids the problem of convergence and slow convergence associated with iterative Markov chain Monte Carlo (MCMC) methods. The idea is to first utilize the sampling inverse Bayes formula (IBF) to derive the conditional distribution of the latent data given the observed data, and then to draw iid samples from the complete-data posterior distribution. For the purpose of selecting the appropriate model (or determining the number of changepoints), we develop two alternative formulae to exactly calculate marginal likelihood (or Bayes factor) by using the exact IBF output and the point-wise IBF, respectively. The HUS data are re-analyzed using the proposed methods. Simulations are implemented to validate the performance of the proposed methods.