On Block Updating in Markov Random Field Models for Disease Mapping

Gaussian Markov random field (GMRF) models are commonly used to model spatial correlation in disease mapping applications. For Bayesian inference by MCMC, so far mainly single-site updating algorithms have been considered. However, convergence and mixing properties of such algorithms can be extremely poor due to strong dependencies of parameters in the posterior distribution. In this paper, we propose various block sampling algorithms in order to improve the MCMC performance. The methodology is rather general, allows for non-standard full conditionals, and can be applied in a modular fashion in a large number of different scenarios. For illustration we consider three different applications: two formulations for spatial modelling of a single disease (with and without additional unstructured parameters respectively), and one formulation for the joint analysis of two diseases. The results indicate that the largest benefits are obtained if parameters and the corresponding hyperparameter are updated jointly in one large block. Implementation of such block algorithms is relatively easy using methods for fast sampling of Gaussian Markov random fields ( Rue, 2001 ). By comparison, Monte Carlo estimates based on single-site updating can be rather misleading, even for very long runs. Our results may have wider relevance for efficient MCMC simulation in hierarchical models with Markov random field components.     

Conditions for Non-confounding and Collapsibility without Knowledge of Completely Constructed Causal Diagrams

In this paper, we discuss several concepts in causal inference in terms of causal diagrams proposed by Pearl (1993 , 1995a , b ), and we give conditions for non-confounding, homogeneity and collapsibility for causal effects without knowledge of a completely constructed causal diagram. We first introduce the concepts of non-confounding, conditional non-confounding, uniform non-confounding, homogeneity, collapsibility and strong collapsibility for causal effects, then we present necessary and sufficient conditions for uniform non-confounding, homegeneity and collapsibilities, and finally we show sufficient conditions for non-confounding, conditional non-confounding and uniform non-confounding.     

区域发展潜力的评价方法及其应用

阐述了区域可持续发展的涵义 ,并从系统的角度对区域可持续发展进行了分析 ,提出了区域发展潜力的概念。构建了区域发展潜力的评价模型。最后对H市的发展潜力进行了评价     

动物遗传育种系列课程双螺旋循环教学模式的建立与实践

根据动物遗传育种系列课程的结构、教学特点、本学科学生的层次性、学科发展和对教师多能性的要求 ,对该系列课程进行了教学内容、教学方式、教学手段的全面改革 ,构建了一套双螺旋循环教学模式 ,即教师与课程的螺旋循环链和学生层次与学习过程的螺旋循环链。这两条“教”、“学”互动的螺旋循环链有机结合 ,每完成一次循环教学过程 ,双螺旋上升一个螺距 ,教师的教学水平提高 ,学生的综合素质和学历提高一个层次。     

多元化计量模式的现实思考--历史成本与公允价值择优并存

多元化计量模式的现实思考——历史成本与公允价值择优并存@谢桂芬$宁波高等专科学校经管系!浙江宁波315010~~     

血管的三维重建

构建了血管的三维重建模型 ,并给出了模型的算法及其求解结果 .     

存在品牌转移者和品牌忠诚者的产品差异模型

分析了存在品牌转移者和忠诚者两类特殊消费群情况下的两阶段水平差异模型，其中品牌转移者在选择产品时只考虑价格因素而不考虑运输成本，品牌忠诚者总是选择某一品牌的产品而不管价格和运输成本。讨论了在两阶段选址－定价博弈模型中纯战略均衡存在的条件。     

并行编程模式及分析

讨论了主从模型、单控制流多数据流、分治策略、数据流水线、投机策略、混合模型六种并行编程模型的结构和工作方式、性能及特点,分析统计了大量应用程序。指出了并行程序设计中,并行编程模式的选择直接影响了并行程序的正确性和效率,而主从模型和单控制流多数据流在整体性能上最优,程序执行效率也最好,是两种最常用的并行编程模型。     

高等农业院校大学科技园的功能、模式及运行机制

大学是科技创新的基地 ,是孕育高科技成果的摇篮。大学科技园的建立 ,为学校科技成果迅速转化为现实生产力提供了一个有效平台。文中从大学科技园的意义出发 ,论述了大学科技园的六大功能和四种模式 ,并提出了大学科技园应实行封闭式管理、开放式运行、企业化运作的机制     

企业知识创新的整合模式

企业知识创新的模式问题一直是知识管理理论中探讨的热点。本文从知识管理理论视角阐述了知识的概念和结构 ,分析了企业知识管理的涵义与战略 ,并在评述现有的企业知识创新模式的基础上 ,提出自己的企业知识创新的整合模式