针对贝叶斯网络参数的近似等式约束,提出采用正态分布构建该类约束的数学模型;然后用Dirichlet分布逼近正态分布,并通过目标优化计算Dirichlet分布的超参数;最后采用贝叶斯最大后验概率(maximum a posterior,MAP)估计方法计算网络参数...针对贝叶斯网络参数的近似等式约束,提出采用正态分布构建该类约束的数学模型;然后用Dirichlet分布逼近正态分布,并通过目标优化计算Dirichlet分布的超参数;最后采用贝叶斯最大后验概率(maximum a posterior,MAP)估计方法计算网络参数值。在不同样本量的数据集下进行实验测试,将本文方法与其他4种主要方法进行比较,结果表明:该方法的参数学习精度都好于其他4种方法,尤其是在样本量较小的情况下。该方法的运行时间高于其他4种方法,但相同样本量的数据集下,学习精度的提高倍数要高于时间增加的倍数。展开更多
Suppose that Y follows a χ^(p)-distribution with n degrees of freedom, and Z is the standardized form of Y. Let f(z,n,p) and F(z,n,p) denote the density function and the distribution function of Z, respectively. In t...Suppose that Y follows a χ^(p)-distribution with n degrees of freedom, and Z is the standardized form of Y. Let f(z,n,p) and F(z,n,p) denote the density function and the distribution function of Z, respectively. In this paper, we obtain the asymptotic expansion for f(z,n,p) and F(z,n,p). The validity of these results is illuminated by some numerical examples. We also investigate the power function of χ^(p)-test by the asymptotic expansion.展开更多
In surveying data processing,we generally suppose that the observational errors distribute normally.In this case the method of least squares can give the minimum variance unbiased estimation of the parameters.The meth...In surveying data processing,we generally suppose that the observational errors distribute normally.In this case the method of least squares can give the minimum variance unbiased estimation of the parameters.The method of least squares does not have the character of robustness,so the use of it will become unsuitable when a few measurements inheriting gross error mix with others.We can use the robust estimating methods that can avoid the influence of gross errors.With this kind of method there is no need to know the exact distribution of the observations.But it will cause other difficulties such as the hypothesis testing for estimated parameters when the sample size is not so big.For non_normally distributed measurements we can suppose they obey the p _norm distribution law.The p _norm distribution is a distributional class,which includes the most frequently used distributions such as the Laplace,Normal and Rectangular ones.This distribution is symmetric and has a kurtosis between 3 and -6/5 when p is larger than 1.Using p _norm distribution to describe the statistical character of the errors,the only assumption is that the error distribution is a symmetric and unimodal curve.This method possesses the property of a kind of self_adapting.But the density function of the p _norm distribution is so complex that it makes the theoretical analysis more difficult.And the troublesome calculation also makes this method not suitable for practice.The research of this paper indicates that the p _norm distribution can be represented by the linear combination of Laplace distribution and normal distribution or by the linear combination of normal distribution and rectangular distribution approximately.Which kind of representation will be taken is according to whether the parameter p is larger than 1 and less than 2 or p is larger than 2.The approximate distribution have the same first four order moments with the exact one.It means that approximate distribution has the same mathematical expectation,variance,skewness and kurtosis w展开更多
The cause of the formal difference of p-norm distribution density functions is analyzed, two problems in the deduction of p-norm formulating are improved, and it is proved that two different forms of p-norm distributi...The cause of the formal difference of p-norm distribution density functions is analyzed, two problems in the deduction of p-norm formulating are improved, and it is proved that two different forms of p-norm distribution density functions are equivalent. This work is useful for popularization and application of the p-norm theory to surveying and mapping.展开更多
Normization, i.e., the system of norms is a structure that defines the group of elements containing the norm values for the requirements of a certain resource. Resources comprise of materials, machines and labor. All ...Normization, i.e., the system of norms is a structure that defines the group of elements containing the norm values for the requirements of a certain resource. Resources comprise of materials, machines and labor. All the requirements of the measure units of the resources are given statically and with the discrete data. Thus, every slight change in the expense list item reference causes a change in norm and our norm is not flexible and features a discrepancy with the real life situations. In order to achieve a higher level of preciseness and to speed up the technological processes of planning and norming the engines of a company that lead to the regulation of the system, the discrete elements of the working （time-related） norms should be replaced by the dynamic ones. This is made possible through setting up norms models that in turn can be presented by formulae in the vectoral system. The use and implementation of the new technologies in terms of production, computer science and cybernetics provides for upgrading the norm requirements. New working tasks in turn require a new norm standardization, which can be applied to the hydrodemolition of concrete constructions by means of water robots that use high pressure water jets.展开更多
文摘针对贝叶斯网络参数的近似等式约束,提出采用正态分布构建该类约束的数学模型;然后用Dirichlet分布逼近正态分布,并通过目标优化计算Dirichlet分布的超参数;最后采用贝叶斯最大后验概率(maximum a posterior,MAP)估计方法计算网络参数值。在不同样本量的数据集下进行实验测试,将本文方法与其他4种主要方法进行比较,结果表明:该方法的参数学习精度都好于其他4种方法,尤其是在样本量较小的情况下。该方法的运行时间高于其他4种方法,但相同样本量的数据集下,学习精度的提高倍数要高于时间增加的倍数。
基金Joint supported by Hubei Provincial Natural Science Foundation and Huangshi of China (2022CFD042)。
文摘Suppose that Y follows a χ^(p)-distribution with n degrees of freedom, and Z is the standardized form of Y. Let f(z,n,p) and F(z,n,p) denote the density function and the distribution function of Z, respectively. In this paper, we obtain the asymptotic expansion for f(z,n,p) and F(z,n,p). The validity of these results is illuminated by some numerical examples. We also investigate the power function of χ^(p)-test by the asymptotic expansion.
文摘In surveying data processing,we generally suppose that the observational errors distribute normally.In this case the method of least squares can give the minimum variance unbiased estimation of the parameters.The method of least squares does not have the character of robustness,so the use of it will become unsuitable when a few measurements inheriting gross error mix with others.We can use the robust estimating methods that can avoid the influence of gross errors.With this kind of method there is no need to know the exact distribution of the observations.But it will cause other difficulties such as the hypothesis testing for estimated parameters when the sample size is not so big.For non_normally distributed measurements we can suppose they obey the p _norm distribution law.The p _norm distribution is a distributional class,which includes the most frequently used distributions such as the Laplace,Normal and Rectangular ones.This distribution is symmetric and has a kurtosis between 3 and -6/5 when p is larger than 1.Using p _norm distribution to describe the statistical character of the errors,the only assumption is that the error distribution is a symmetric and unimodal curve.This method possesses the property of a kind of self_adapting.But the density function of the p _norm distribution is so complex that it makes the theoretical analysis more difficult.And the troublesome calculation also makes this method not suitable for practice.The research of this paper indicates that the p _norm distribution can be represented by the linear combination of Laplace distribution and normal distribution or by the linear combination of normal distribution and rectangular distribution approximately.Which kind of representation will be taken is according to whether the parameter p is larger than 1 and less than 2 or p is larger than 2.The approximate distribution have the same first four order moments with the exact one.It means that approximate distribution has the same mathematical expectation,variance,skewness and kurtosis w
基金Supported by Scientific Research Fund of Hunan Province Education Department (No.03C483) .
文摘The cause of the formal difference of p-norm distribution density functions is analyzed, two problems in the deduction of p-norm formulating are improved, and it is proved that two different forms of p-norm distribution density functions are equivalent. This work is useful for popularization and application of the p-norm theory to surveying and mapping.
文摘Normization, i.e., the system of norms is a structure that defines the group of elements containing the norm values for the requirements of a certain resource. Resources comprise of materials, machines and labor. All the requirements of the measure units of the resources are given statically and with the discrete data. Thus, every slight change in the expense list item reference causes a change in norm and our norm is not flexible and features a discrepancy with the real life situations. In order to achieve a higher level of preciseness and to speed up the technological processes of planning and norming the engines of a company that lead to the regulation of the system, the discrete elements of the working （time-related） norms should be replaced by the dynamic ones. This is made possible through setting up norms models that in turn can be presented by formulae in the vectoral system. The use and implementation of the new technologies in terms of production, computer science and cybernetics provides for upgrading the norm requirements. New working tasks in turn require a new norm standardization, which can be applied to the hydrodemolition of concrete constructions by means of water robots that use high pressure water jets.
