We consider sparsity selection for the Cholesky factor L of the inverse covariance matrix in high-dimensional Gaussian DAG models.The sparsity is induced over the space of L via non-local priors,namely the product mom...We consider sparsity selection for the Cholesky factor L of the inverse covariance matrix in high-dimensional Gaussian DAG models.The sparsity is induced over the space of L via non-local priors,namely the product moment(pMOM)prior[Johnson,V.,&Rossell,D.(2012).Bayesian model selection in high-dimensional settings.Journal of the American Statistical Asso-ciation,107(498),649-660.https://doi.org/10.1080/01621459.2012.682536]and the hierarchi-cal hyper-pMOM prior[Cao,X.,Khare,K.,&Ghosh,M.(2020).High-dimensional posterior consistency for hierarchical non-local priors in regression.Bayesian Analysis,15(1),241-262.https://doi.org/10.1214/19-BA1154].We establish model selection consistency for Cholesky fac-tor under more relaxed conditions compared to those in the literature and implement an efficient MCMC algorithm for parallel selecting the sparsity pattern for each column of L.We demonstrate the validity of our theoretical results via numerical simulations,and also use further simulations to demonstrate that our sparsity selection approach is competitive with existing methods.展开更多
将云计算和工作流两者结合起来,并根据用户关心的QoS中执行时间和执行费用问题,针对工作流调度策略在云环境下调度实例密集型工作流时效率不高的问题优化资源调度策略,给出云工作流调度模型,提出一种基于QoS约束的云工作流调度算法MSCWQ...将云计算和工作流两者结合起来,并根据用户关心的QoS中执行时间和执行费用问题,针对工作流调度策略在云环境下调度实例密集型工作流时效率不高的问题优化资源调度策略,给出云工作流调度模型,提出一种基于QoS约束的云工作流调度算法MSCWQ(modified scheduling algorithm for cloud workflow based on QoS).该算法利用DAG(directed acyclic graph)进行建模,优化资源策略,保证在最晚结束时间内使整个工作流实例的执行费用尽可能小.实验结果表明,在调度实例密集型云工作流时,该算法能有效提升科学工作流的执行效率,并能减少资源的使用费用.展开更多
基金This work was supported by Simons Foundation’s collabora-tion grant(No.635213).
文摘We consider sparsity selection for the Cholesky factor L of the inverse covariance matrix in high-dimensional Gaussian DAG models.The sparsity is induced over the space of L via non-local priors,namely the product moment(pMOM)prior[Johnson,V.,&Rossell,D.(2012).Bayesian model selection in high-dimensional settings.Journal of the American Statistical Asso-ciation,107(498),649-660.https://doi.org/10.1080/01621459.2012.682536]and the hierarchi-cal hyper-pMOM prior[Cao,X.,Khare,K.,&Ghosh,M.(2020).High-dimensional posterior consistency for hierarchical non-local priors in regression.Bayesian Analysis,15(1),241-262.https://doi.org/10.1214/19-BA1154].We establish model selection consistency for Cholesky fac-tor under more relaxed conditions compared to those in the literature and implement an efficient MCMC algorithm for parallel selecting the sparsity pattern for each column of L.We demonstrate the validity of our theoretical results via numerical simulations,and also use further simulations to demonstrate that our sparsity selection approach is competitive with existing methods.
文摘将云计算和工作流两者结合起来,并根据用户关心的QoS中执行时间和执行费用问题,针对工作流调度策略在云环境下调度实例密集型工作流时效率不高的问题优化资源调度策略,给出云工作流调度模型,提出一种基于QoS约束的云工作流调度算法MSCWQ(modified scheduling algorithm for cloud workflow based on QoS).该算法利用DAG(directed acyclic graph)进行建模,优化资源策略,保证在最晚结束时间内使整个工作流实例的执行费用尽可能小.实验结果表明,在调度实例密集型云工作流时,该算法能有效提升科学工作流的执行效率,并能减少资源的使用费用.
