OpenBUGS软件是在WinBUGS软件基础上研制的一款实现贝叶斯统计推断的工具软件,它是以MCMC（M arkov Chain Monte Carlo）方法为基础,将所有未知或不确定的参数都视为随机变量,并对此种类型的概率模型进行求解。它广泛地应用于医学、经...OpenBUGS软件是在WinBUGS软件基础上研制的一款实现贝叶斯统计推断的工具软件,它是以MCMC（M arkov Chain Monte Carlo）方法为基础,将所有未知或不确定的参数都视为随机变量,并对此种类型的概率模型进行求解。它广泛地应用于医学、经济学、生命科学、心理学、社会科学等多个领域。展开更多
贝叶斯统计学与频率统计学是当今世界主要的两大统计学派,二者在统计推断的理论和方法上存在较大差异。随着现代计算机的高速发展,贝叶斯统计的研究不再只停滞于理论阶段,马尔可夫链蒙特卡罗（Markov Chain Monte Carlo,MCMC）方法的应...贝叶斯统计学与频率统计学是当今世界主要的两大统计学派,二者在统计推断的理论和方法上存在较大差异。随着现代计算机的高速发展,贝叶斯统计的研究不再只停滞于理论阶段,马尔可夫链蒙特卡罗（Markov Chain Monte Carlo,MCMC）方法的应用,解决了后验分布复杂高维计算的瓶颈问题,使得贝叶斯统计在理论和方法上均取得了快速发展[1]。展开更多
目的针对分层抽样流行病调查数据的结构特点,构建两种基于分层嵌套思想的贝叶斯层次模型,并探讨其优缺点。方法以贝叶斯层次模型为基础,利用嵌套结构中的层级关系构建模型,其中,模型一以嵌套层效应分解为特点构建,模型二以嵌套层效应逐...目的针对分层抽样流行病调查数据的结构特点,构建两种基于分层嵌套思想的贝叶斯层次模型,并探讨其优缺点。方法以贝叶斯层次模型为基础,利用嵌套结构中的层级关系构建模型,其中,模型一以嵌套层效应分解为特点构建,模型二以嵌套层效应逐级传递为特点构建。以重庆市出生缺陷调查数据为例,采用Open BUGS软件进行模型拟合及分析。结果以偏差信息准则(deviance information criterion,DIC)作为拟合优度评价,模型一和模型二的DIC值分别为101.8和101.6,大致相等;敏感性分析显示,在总体率的超参数μ设置不同先验信息下,模型一和模型二对总效应估计的变异性分别为(用标准差度量,10-4):后验均数1.191和27.546;后验中位数1.038和7.617,模型一的变异性比模型二小。结论模型一和模型二均可用于嵌套结构的调查数据建模分析及预测,拟合效果相当;但模型一比模型二受先验信息影响小,稳健性更好,更适合先验信息欠缺时的数据分析。展开更多
The purpose of the research is to analyze the new Spanish law of Traffic, which no longer permits exceeding by up to 20 km/hour the generic speed limits when overtaking on conventional roads. In this research, determi...The purpose of the research is to analyze the new Spanish law of Traffic, which no longer permits exceeding by up to 20 km/hour the generic speed limits when overtaking on conventional roads. In this research, deterministic and random models are developed to analyze the associated safety risks. The deterministic model highlights the importance of dimensional analysis and provides dimensionless abacuses to analyze the problem. Next, Bayesian networks and Bayesian models are used to build a random model from the previous one, providing a general method to convert deterministic into random models. In addition, the problems of ignoring the dimensions of the variables and parameters are discussed, a common mistake to be corrected. Some examples and multidimensional graphics illustrate the huge reduction in safety and the need to review the existing end of prohibition signs, most of which must be removed. Shortly, the research results show that the distance required for overtaking with safety increases drastically.展开更多
In this work, we study some computational aspects for the Bayesian analysis involving stable distributions. It is well known that, in general, there is no closed form for the probability density function of stable dis...In this work, we study some computational aspects for the Bayesian analysis involving stable distributions. It is well known that, in general, there is no closed form for the probability density function of stable distributions. However, the use of a latent or auxiliary random variable facilitates to obtain any posterior distribution when being related to stable distributions. To show the usefulness of the computational aspects, the methodology is applied to two examples: one is related to daily price returns of Abbey National shares, considered in [1], and the other is the length distribution analysis of coding and non-coding regions in a Homo sapiens chromosome DNA sequence, considered in [2]. Posterior summaries of interest are obtained using the OpenBUGS software.展开更多
In this paper, we study some robustness aspects of linear regression models of the presence of outliers or discordant observations considering the use of stable distributions for the response in place of the usual nor...In this paper, we study some robustness aspects of linear regression models of the presence of outliers or discordant observations considering the use of stable distributions for the response in place of the usual normality assumption. It is well known that, in general, there is no closed form for the probability density function of stable distributions. However, under a Bayesian approach, the use of a latent or auxiliary random variable gives some simplification to obtain any posterior distribution when related to stable distributions. To show the usefulness of the computational aspects, the methodology is applied to two examples: one is related to a standard linear regression model with an explanatory variable and the other is related to a simulated data set assuming a 23 factorial experiment. Posterior summaries of interest are obtained using MCMC (Markov Chain Monte Carlo) methods and the OpenBugs software.展开更多
文摘OpenBUGS软件是在WinBUGS软件基础上研制的一款实现贝叶斯统计推断的工具软件,它是以MCMC（M arkov Chain Monte Carlo）方法为基础,将所有未知或不确定的参数都视为随机变量,并对此种类型的概率模型进行求解。它广泛地应用于医学、经济学、生命科学、心理学、社会科学等多个领域。
文摘贝叶斯统计学与频率统计学是当今世界主要的两大统计学派,二者在统计推断的理论和方法上存在较大差异。随着现代计算机的高速发展,贝叶斯统计的研究不再只停滞于理论阶段,马尔可夫链蒙特卡罗（Markov Chain Monte Carlo,MCMC）方法的应用,解决了后验分布复杂高维计算的瓶颈问题,使得贝叶斯统计在理论和方法上均取得了快速发展[1]。
文摘目的针对分层抽样流行病调查数据的结构特点,构建两种基于分层嵌套思想的贝叶斯层次模型,并探讨其优缺点。方法以贝叶斯层次模型为基础,利用嵌套结构中的层级关系构建模型,其中,模型一以嵌套层效应分解为特点构建,模型二以嵌套层效应逐级传递为特点构建。以重庆市出生缺陷调查数据为例,采用Open BUGS软件进行模型拟合及分析。结果以偏差信息准则(deviance information criterion,DIC)作为拟合优度评价,模型一和模型二的DIC值分别为101.8和101.6,大致相等;敏感性分析显示,在总体率的超参数μ设置不同先验信息下,模型一和模型二对总效应估计的变异性分别为(用标准差度量,10-4):后验均数1.191和27.546;后验中位数1.038和7.617,模型一的变异性比模型二小。结论模型一和模型二均可用于嵌套结构的调查数据建模分析及预测,拟合效果相当;但模型一比模型二受先验信息影响小,稳健性更好,更适合先验信息欠缺时的数据分析。
文摘The purpose of the research is to analyze the new Spanish law of Traffic, which no longer permits exceeding by up to 20 km/hour the generic speed limits when overtaking on conventional roads. In this research, deterministic and random models are developed to analyze the associated safety risks. The deterministic model highlights the importance of dimensional analysis and provides dimensionless abacuses to analyze the problem. Next, Bayesian networks and Bayesian models are used to build a random model from the previous one, providing a general method to convert deterministic into random models. In addition, the problems of ignoring the dimensions of the variables and parameters are discussed, a common mistake to be corrected. Some examples and multidimensional graphics illustrate the huge reduction in safety and the need to review the existing end of prohibition signs, most of which must be removed. Shortly, the research results show that the distance required for overtaking with safety increases drastically.
基金partially supported by CNPq-Brazil,by CAPES-Brazil,by INCT em Matematica and also by Pronex Probabilidade e Processos Estocasticos-E-26/170.008/2008-APQ1the financial support from the Conselho Nacional de Desenvolvimento Cientifico e Tecnologico(CNPq).
文摘In this work, we study some computational aspects for the Bayesian analysis involving stable distributions. It is well known that, in general, there is no closed form for the probability density function of stable distributions. However, the use of a latent or auxiliary random variable facilitates to obtain any posterior distribution when being related to stable distributions. To show the usefulness of the computational aspects, the methodology is applied to two examples: one is related to daily price returns of Abbey National shares, considered in [1], and the other is the length distribution analysis of coding and non-coding regions in a Homo sapiens chromosome DNA sequence, considered in [2]. Posterior summaries of interest are obtained using the OpenBUGS software.
基金financial support from the Brazilian Institution Conselho Nacional de Desenvolvimento Cientifico e Tecnologico(CNPq).
文摘In this paper, we study some robustness aspects of linear regression models of the presence of outliers or discordant observations considering the use of stable distributions for the response in place of the usual normality assumption. It is well known that, in general, there is no closed form for the probability density function of stable distributions. However, under a Bayesian approach, the use of a latent or auxiliary random variable gives some simplification to obtain any posterior distribution when related to stable distributions. To show the usefulness of the computational aspects, the methodology is applied to two examples: one is related to a standard linear regression model with an explanatory variable and the other is related to a simulated data set assuming a 23 factorial experiment. Posterior summaries of interest are obtained using MCMC (Markov Chain Monte Carlo) methods and the OpenBugs software.
