1)Institute of Science, Information Engineering University, Zhengzhou 450001;2)Institute of Surveying and Mapping, Information Engineering University, Zhengzhou 450052
Abstract With the view of influence analysis, a new Bayesian method for gross errors detection based on divergence of KullbackLeiber is proposed. Under the condition of unequal weight and independent observations, a comparison among the data deletion model, the variance inflation model and the mean shift model based on certain prior distribution is made, and the computational formula of divergence of KullbackLeiber about the three models is given and a judge rule of gross error detection is established. Finally, the method is used for gross error detection in triangulateration network and a good result is obtained.
Wang Yanting ,Gui Qingming,and Zhang Qianqian . BAYESIAN APPROACH TO DETECTION OF GROSS ERRORS BASED ON DIVERGENCE OF KULLBACK-LEIBER[J]. , 2012, 32(2): 51-54.
Wang Yanting ,Gui Qingming,and Zhang Qianqian . BAYESIAN APPROACH TO DETECTION OF GROSS ERRORS BASED ON DIVERGENCE OF KULLBACK-LEIBER[J]. jgg, 2012, 32(2): 51-54.
