THE COLLAPSED GIBBS SAMPLER IN BAYESIAN COMPUTATIONS WITH APPLICATIONS TO A GENE-REGULATION PROBLEM
THE COLLAPSED GIBBS SAMPLER IN BAYESIAN COMPUTATIONS WITH APPLICATIONS TO A GENE-REGULATION PROBLEM
复制标题
DOI：
10.2307/2290921
复制
发表时间：
1994-09-01
影响因子：
3.7
通讯作者：
LIU, JS
中科院分区：
文献类型：
--
作者：
LIU, JS
本文介绍了吉布斯抽样器的一种“分组"和”折叠“方法，并从算子论的观点证明了这种方法的普遍性。与相应的不可逆马尔可夫链的前向运营商的规范被用来区分不同的模拟方案。当应用于贝叶斯缺失数据问题时，折叠的想法建议跳过标准数据增强中的采样参数值的步骤。通过这样做，我们获得了吉布斯采样器的预测更新版本。给出了在不完全观测数据情况下，利用Gibbs抽样方法计算后验比值比的方法。作为可能的应用的说明，提供了三个例子，沿着贝叶斯处理用于识别未比对DNA序列中的常见蛋白质结合位点。
This article describes a method of ''grouping'' and ''collapsing'' in using the Gibbs sampler and proves from an operator theory viewpoint that the method is in general beneficial. The norms of the forward operators associated with the corresponding nonreversible Markov chains are used to discriminate among different simulation schemes. When applied to Bayesian missing data problems, the idea of collapsing suggests skipping the steps of sampling parameter(s) values in standard data augmentation. By doing this, we obtain a predictive update version of the Gibbs sampler. A procedure of calculating the posterior odds ratio via the collapsed Gibbs sampler when incomplete observations are involved is presented. As an illustration of possible applications, three examples, along with a Bayesian treatment for identifying common protein binding sites in unaligned DNA sequences, are provided.