The topic of this article is one-sided hypothesis testing for disparity, i.e., the mean of one group is larger than that of another when there is uncertainty as to which group a datum is drawn. For each datum, the unc...The topic of this article is one-sided hypothesis testing for disparity, i.e., the mean of one group is larger than that of another when there is uncertainty as to which group a datum is drawn. For each datum, the uncertainty is captured with a given discrete probability distribution over the groups. Such situations arise, for example, in the use of Bayesian imputation methods to assess race and ethnicity disparities with certain insurance, health, and financial data. A widely used method to implement this assessment is the Bayesian Improved Surname Geocoding (BISG) method which assigns a discrete probability over six race/ethnicity groups to an individual given the individual’s surname and address location. Using a Bayesian framework and Markov Chain Monte Carlo sampling from the joint posterior distribution of the group means, the probability of a disparity hypothesis is estimated. Four methods are developed and compared with an illustrative data set. Three of these methods are implemented in an R-code and one method in WinBUGS. These methods are programed for any number of groups between two and six inclusive. All the codes are provided in the appendices.展开更多
在现有的图聚类方法中,大多数聚类方法只关注图的拓扑结构或节点属性而忽略另一方面.为解决这一问题,相关文献中提出了基于图的结构与属性的图聚类方法.但这些聚类方法存在建立的图模型不准确、聚类效果不理想、算法执行效率低等缺点....在现有的图聚类方法中,大多数聚类方法只关注图的拓扑结构或节点属性而忽略另一方面.为解决这一问题,相关文献中提出了基于图的结构与属性的图聚类方法.但这些聚类方法存在建立的图模型不准确、聚类效果不理想、算法执行效率低等缺点.针对上述图聚类方法中存在的问题,提出了一种基于结构-属性的时空对象图聚类方法(spatio-temporal object graph clustering algorithm based on structure and attribute,STSA).首先提出了属性加权图模型,在此基础上建立了结构-属性的统一度量方法,并采用随机游走模型技术将节点间结构与属性关系转换为相应的相似度矩阵,结合图结构-属性关系及相似度矩阵,采用信息传递算法对图进行聚类,解决了现有图聚类方法中所存在的问题,最后通过实验验证了提出的STSA方法的正确性和有效性.展开更多
A random walk Metropolis-Hastings algorithm has been widely used in sampling the parameter of spatial interaction in spatial autoregressive model from a Bayesian point of view. In addition, as an alternative approach,...A random walk Metropolis-Hastings algorithm has been widely used in sampling the parameter of spatial interaction in spatial autoregressive model from a Bayesian point of view. In addition, as an alternative approach, the griddy Gibbs sampler is proposed by [1] and utilized by [2]. This paper proposes an acceptance-rejection Metropolis-Hastings algorithm as a third approach, and compares these three algorithms through Monte Carlo experiments. The experimental results show that the griddy Gibbs sampler is the most efficient algorithm among the algorithms whether the number of observations is small or not in terms of the computation time and the inefficiency factors. Moreover, it seems to work well when the size of grid is 100.展开更多
针对人为选定参数造成神经网络故障诊断性能不稳定问题与麻雀搜索算法(sparrow search algorithm,SSA)种群初始化时由于随机性造成寻优范围缩小和算法容易陷入局部最优等问题,采用反向学习(opposition-based learning,OBL)对SSA算法中...针对人为选定参数造成神经网络故障诊断性能不稳定问题与麻雀搜索算法(sparrow search algorithm,SSA)种群初始化时由于随机性造成寻优范围缩小和算法容易陷入局部最优等问题,采用反向学习(opposition-based learning,OBL)对SSA算法中麻雀种群初始化过程进行优化,扩大搜索范围,并结合随机游走策略(random walk,RW)对寻优过程中的最优麻雀施加扰动,提高算法的局部搜索能力,降低算法陷入局部最优的风险。在此基础上,采用基于反向学习和随机游走策略的麻雀搜索算法(sparrow search algorithm based on oppositionbased learning and random walk,BRWSSA)优化门控循环单元(gate recurrent unit,GRU)的隐含层节点个数,设计了一种基于BRWSSA-GRU的发动机滑油系统故障诊断模型。为了验证所设计的故障诊断模型的有效性,还设计了GRU和SSA-GRU两种故障诊断模型。最后,采用相同的滑油系统数据集对GRU、SSA-GRU和BRWSSA-GRU3种不同的故障诊断模型的有效性进行了对比试验验证。结果表明,提出的BRWSSA-GRU故障诊断模型的诊断准确率明显优于GRU和SSA-GRU方法,BRWSSA-GRU故障诊断模型的有效性得到验证。展开更多
文摘The topic of this article is one-sided hypothesis testing for disparity, i.e., the mean of one group is larger than that of another when there is uncertainty as to which group a datum is drawn. For each datum, the uncertainty is captured with a given discrete probability distribution over the groups. Such situations arise, for example, in the use of Bayesian imputation methods to assess race and ethnicity disparities with certain insurance, health, and financial data. A widely used method to implement this assessment is the Bayesian Improved Surname Geocoding (BISG) method which assigns a discrete probability over six race/ethnicity groups to an individual given the individual’s surname and address location. Using a Bayesian framework and Markov Chain Monte Carlo sampling from the joint posterior distribution of the group means, the probability of a disparity hypothesis is estimated. Four methods are developed and compared with an illustrative data set. Three of these methods are implemented in an R-code and one method in WinBUGS. These methods are programed for any number of groups between two and six inclusive. All the codes are provided in the appendices.
文摘在现有的图聚类方法中,大多数聚类方法只关注图的拓扑结构或节点属性而忽略另一方面.为解决这一问题,相关文献中提出了基于图的结构与属性的图聚类方法.但这些聚类方法存在建立的图模型不准确、聚类效果不理想、算法执行效率低等缺点.针对上述图聚类方法中存在的问题,提出了一种基于结构-属性的时空对象图聚类方法(spatio-temporal object graph clustering algorithm based on structure and attribute,STSA).首先提出了属性加权图模型,在此基础上建立了结构-属性的统一度量方法,并采用随机游走模型技术将节点间结构与属性关系转换为相应的相似度矩阵,结合图结构-属性关系及相似度矩阵,采用信息传递算法对图进行聚类,解决了现有图聚类方法中所存在的问题,最后通过实验验证了提出的STSA方法的正确性和有效性.
文摘A random walk Metropolis-Hastings algorithm has been widely used in sampling the parameter of spatial interaction in spatial autoregressive model from a Bayesian point of view. In addition, as an alternative approach, the griddy Gibbs sampler is proposed by [1] and utilized by [2]. This paper proposes an acceptance-rejection Metropolis-Hastings algorithm as a third approach, and compares these three algorithms through Monte Carlo experiments. The experimental results show that the griddy Gibbs sampler is the most efficient algorithm among the algorithms whether the number of observations is small or not in terms of the computation time and the inefficiency factors. Moreover, it seems to work well when the size of grid is 100.
文摘针对人为选定参数造成神经网络故障诊断性能不稳定问题与麻雀搜索算法(sparrow search algorithm,SSA)种群初始化时由于随机性造成寻优范围缩小和算法容易陷入局部最优等问题,采用反向学习(opposition-based learning,OBL)对SSA算法中麻雀种群初始化过程进行优化,扩大搜索范围,并结合随机游走策略(random walk,RW)对寻优过程中的最优麻雀施加扰动,提高算法的局部搜索能力,降低算法陷入局部最优的风险。在此基础上,采用基于反向学习和随机游走策略的麻雀搜索算法(sparrow search algorithm based on oppositionbased learning and random walk,BRWSSA)优化门控循环单元(gate recurrent unit,GRU)的隐含层节点个数,设计了一种基于BRWSSA-GRU的发动机滑油系统故障诊断模型。为了验证所设计的故障诊断模型的有效性,还设计了GRU和SSA-GRU两种故障诊断模型。最后,采用相同的滑油系统数据集对GRU、SSA-GRU和BRWSSA-GRU3种不同的故障诊断模型的有效性进行了对比试验验证。结果表明,提出的BRWSSA-GRU故障诊断模型的诊断准确率明显优于GRU和SSA-GRU方法,BRWSSA-GRU故障诊断模型的有效性得到验证。
