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  average estimation
Testing the new model for radiometric measurements showed that the average estimation error for 10 varieties under early rice conditions was less than 1%.
      
The average estimation error from patient data loop control of nine patient treatments.
      
The average estimation error from patient data analysis of 21 sites at which temperature was independently measured (three per patient) was 0·0°C, with a standard deviation of 0·8°C.
      
The average estimation errors were 7% and 9% when two and three synthetic radiographic images obtained at different x-ray tube settings were used.
      
An extension of this multi-point conditional average estimation is presented in this paper in the form of a pseudo-dynamic reconstruction of the flow field.
      
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  average estimation
Testing the new model for radiometric measurements showed that the average estimation error for 10 varieties under early rice conditions was less than 1%.
      
The average estimation error from patient data loop control of nine patient treatments.
      
The average estimation error from patient data analysis of 21 sites at which temperature was independently measured (three per patient) was 0·0°C, with a standard deviation of 0·8°C.
      
The average estimation errors were 7% and 9% when two and three synthetic radiographic images obtained at different x-ray tube settings were used.
      
An extension of this multi-point conditional average estimation is presented in this paper in the form of a pseudo-dynamic reconstruction of the flow field.
      
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Positron emission tomography(PET) is a noisy functional medical imaging model. In this paper a fully Bayesian PET reconstruction method is presented for combining a segmented anatomical membrane a priori. The segmented anatomical membrane a priori is based on the fact that the radiopharmaceutical activity is similar throughout each region and the anatomical information can be obtained from other imaging modalities such as CT or MRI. The prior distributions are formed as some kind of Markov random field. Due...

Positron emission tomography(PET) is a noisy functional medical imaging model. In this paper a fully Bayesian PET reconstruction method is presented for combining a segmented anatomical membrane a priori. The segmented anatomical membrane a priori is based on the fact that the radiopharmaceutical activity is similar throughout each region and the anatomical information can be obtained from other imaging modalities such as CT or MRI. The prior distributions are formed as some kind of Markov random field. Due the non convex and the hyper parameters in the prior, it is difficult to use point estimator such as maximum a posteriori(MAP). So we used Dynamic Markov chain Monte Carlo posterior simulation method to get a minimum mean square error(MMSE) estimator which update the hyper parameters as well as density data. The variances and credit area of the reconstruction results can be easy gotten by MMSE. We compared the reconstruction result of ML, MAP and MMSE, and find that the segmented anatomical membrane a priori exhibit improved the noise and resolution properties and Dynamic Markov chain Monte Carlo is mostly suitable for fully Bayesian reconstruction.

正电子发射成像是一种有效的生理功能性成像手段 ,但是由于投影数据中噪声大而给重建带来困难 ,为此提出了利用其他高质量的解剖成像结果构造的分割模板先验来进行完全 Bayesian重建以提高重建效果 .分割模板先验可以表示为包含超验参数的 Markov场形式 ,但是它的非凸性和超验参数的存在使得无法用常规的方法得到最大后验估计 ,为此采用动态后验模拟算法计算后验平均估计 .基于满足条件分布的动态后验模拟法可以同时更新象素的密度和超验参数 ,并且容易得到重建的方差和置信区间 .将这种方法和似然估计、最大后验估计结果进行比较 ,重建的结果无论在空间分辨率和抑制噪声方面都有取得了好的效果 .

T(n,e,α)=T(p,q,e,α) denoting the number of α-oder fixed points of RSA(p,q,e) , one kind of estimation on logT(p,q,e,α) is gived, when e runs over a set. Let L_1 and L_2 be large integers,L_2>L_1 , and S(n,α)=1L_2-L_1L_2e=L_1,(e,(n))=1logT(n,e,α), two equalities of S(n,1) and S(n,2) are given.

以T(n,e,α)=T(p,q,e,α)表示RSA(n,e)的α阶不动点的个数,讨论了logT(p,q,e,α)的一种平均估计.设L1和L2为充分大的整数,L2>L1,定义S(n,α)=L2-1L1∑L2e=L1,(e,φ(n)=1)logT(n,e,α),给出了S(n,1)和S(n,2)的两个等式.

To estimate null value in the database is a main task of data-processing,first it gets the cloud models which present the raw nonempty value in the database,then recoves the value with the cloud to replace the null value.The method considers the original data’s distribution sufficiently and can simulate the humans’ behavior,so it can estimate the null value more precisely.

数据库中空值的估计是数据处理的一项重要内容,数值型数据的云模型空值估计方法是将数据库中之数据先作云的数字化处理,然后利用云模型产生空值替代值,这种估计空值的新方法尊重原始数据的分布特征,具有人类自然行为的特征,比其它的方法具有更高的平均估计准确率。

 
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