具备可重配置流水线总线的线性阵列LARPBS(linear arrays with a reconfigurable pipelined bus systems)是近来出现的一种高效的并行计算模型,与理想的PRAM模型不同,LARPBS是现实可行的。基于LARPBS模型,Y.Pan介绍了2种宽度和精度任意...具备可重配置流水线总线的线性阵列LARPBS(linear arrays with a reconfigurable pipelined bus systems)是近来出现的一种高效的并行计算模型,与理想的PRAM模型不同,LARPBS是现实可行的。基于LARPBS模型,Y.Pan介绍了2种宽度和精度任意的数据项的最大值查找算法:算法1使用了N^2/2个处理机、O(1)时间,它是目前时间最优的算法;算法2使用了N个处理机、O(loglogN)时间。本文介绍了2种最大值查找算法,时间复杂度同Y.Pan的算法,但所用处理机数减少了一半,这是对Y.Pan算法的重要改进。展开更多
In standard interval mapping (IM) of quantitative trait loci (QTL), the QTL effect is described by a normal mixture model. When this assumption of normality is violated, the most commonly adopted strategy is to use th...In standard interval mapping (IM) of quantitative trait loci (QTL), the QTL effect is described by a normal mixture model. When this assumption of normality is violated, the most commonly adopted strategy is to use the previous model after data transformation. However, an appropriate transformation may not exist or may be difficult to find. Also this approach can raise interpretation issues. An interesting alternative is to consider a skew-normal mixture model in standard IM, and the resulting method is here denoted as skew-normal IM. This flexible model that includes the usual symmetric normal distribution as a special case is important, allowing continuous variation from normality to non-normality. In this paper we briefly introduce the main peculiarities of the skew-normal distribution. The maximum likelihood estimates of parameters of the skew-normal distribution are obtained by the expectation-maximization (EM) algorithm. The proposed model is illustrated with real data from an intercross experiment that shows a significant departure from the normality assumption. The performance of the skew-normal IM is assessed via stochastic simulation. The results indicate that the skew-normal IM has higher power for QTL detection and better precision of QTL location as compared to standard IM and nonparametric IM.展开更多
文摘具备可重配置流水线总线的线性阵列LARPBS(linear arrays with a reconfigurable pipelined bus systems)是近来出现的一种高效的并行计算模型,与理想的PRAM模型不同,LARPBS是现实可行的。基于LARPBS模型,Y.Pan介绍了2种宽度和精度任意的数据项的最大值查找算法:算法1使用了N^2/2个处理机、O(1)时间,它是目前时间最优的算法;算法2使用了N个处理机、O(loglogN)时间。本文介绍了2种最大值查找算法,时间复杂度同Y.Pan的算法,但所用处理机数减少了一半,这是对Y.Pan算法的重要改进。
基金Project supported in part by Foundation for Science and Technology(FCT) (No.SFRD/BD/5987/2001)the Operational ProgramScience,Technology,and Innovation of the FCT,co-financed by theEuropean Regional Development Fund (ERDF)
文摘In standard interval mapping (IM) of quantitative trait loci (QTL), the QTL effect is described by a normal mixture model. When this assumption of normality is violated, the most commonly adopted strategy is to use the previous model after data transformation. However, an appropriate transformation may not exist or may be difficult to find. Also this approach can raise interpretation issues. An interesting alternative is to consider a skew-normal mixture model in standard IM, and the resulting method is here denoted as skew-normal IM. This flexible model that includes the usual symmetric normal distribution as a special case is important, allowing continuous variation from normality to non-normality. In this paper we briefly introduce the main peculiarities of the skew-normal distribution. The maximum likelihood estimates of parameters of the skew-normal distribution are obtained by the expectation-maximization (EM) algorithm. The proposed model is illustrated with real data from an intercross experiment that shows a significant departure from the normality assumption. The performance of the skew-normal IM is assessed via stochastic simulation. The results indicate that the skew-normal IM has higher power for QTL detection and better precision of QTL location as compared to standard IM and nonparametric IM.
