目的:探索一种适用于药品检验常见检测项目及方法的不确定度评定方式。方法:以国际标准方式,建立数学模型,分析不确定度来源,计算各项目方法的不确定度。结果:研究并探索了中国药典附录常用检测项目方法的不确定度评定方式,计算出了各...目的:探索一种适用于药品检验常见检测项目及方法的不确定度评定方式。方法:以国际标准方式,建立数学模型,分析不确定度来源,计算各项目方法的不确定度。结果:研究并探索了中国药典附录常用检测项目方法的不确定度评定方式,计算出了各检测项目方法的不确定度范围。结论:提出了本系统常用检测方法的不确定度 B 类评定基本方式,为进一步研究奠定了基础。展开更多
Overlapping community detection has become a very hot research topic in recent decades,and a plethora of methods have been proposed.But,a common challenge in many existing overlapping community detection approaches is...Overlapping community detection has become a very hot research topic in recent decades,and a plethora of methods have been proposed.But,a common challenge in many existing overlapping community detection approaches is that the number of communities K must be predefined manually.We propose a flexible nonparametric Bayesian generative model for count-value networks,which can allow K to increase as more and more data are encountered instead of to be fixed in advance.The Indian buffet process was used to model the community assignment matrix Z,and an uncol-lapsed Gibbs sampler has been derived.However,as the community assignment matrix Zis a structured multi-variable parameter,how to summarize the posterior inference results andestimate the inference quality about Z,is still a considerable challenge in the literature.In this paper,a graph convolutional neural network based graph classifier was utilized to help tosummarize the results and to estimate the inference qualityabout Z.We conduct extensive experiments on synthetic data and real data,and find that empirically,the traditional posterior summarization strategy is reliable.展开更多
文摘目的:探索一种适用于药品检验常见检测项目及方法的不确定度评定方式。方法:以国际标准方式,建立数学模型,分析不确定度来源,计算各项目方法的不确定度。结果:研究并探索了中国药典附录常用检测项目方法的不确定度评定方式,计算出了各检测项目方法的不确定度范围。结论:提出了本系统常用检测方法的不确定度 B 类评定基本方式,为进一步研究奠定了基础。
基金supported by the National Basic Research Program of China(973)(2012CB316402)The National Natural Science Foundation of China(Grant Nos.61332005,61725205)+3 种基金The Research Project of the North Minzu University(2019XYZJK02,2019xYZJK05,2017KJ24,2017KJ25,2019MS002)Ningxia first-classdisciplinc and scientific research projects(electronic science and technology,NXYLXK2017A07)NingXia Provincial Key Discipline Project-Computer ApplicationThe Provincial Natural Science Foundation ofNingXia(NZ17111,2020AAC03219).
文摘Overlapping community detection has become a very hot research topic in recent decades,and a plethora of methods have been proposed.But,a common challenge in many existing overlapping community detection approaches is that the number of communities K must be predefined manually.We propose a flexible nonparametric Bayesian generative model for count-value networks,which can allow K to increase as more and more data are encountered instead of to be fixed in advance.The Indian buffet process was used to model the community assignment matrix Z,and an uncol-lapsed Gibbs sampler has been derived.However,as the community assignment matrix Zis a structured multi-variable parameter,how to summarize the posterior inference results andestimate the inference quality about Z,is still a considerable challenge in the literature.In this paper,a graph convolutional neural network based graph classifier was utilized to help tosummarize the results and to estimate the inference qualityabout Z.We conduct extensive experiments on synthetic data and real data,and find that empirically,the traditional posterior summarization strategy is reliable.
