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prior distribution相关的网络例句

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与 prior distribution 相关的网络例句 [注:此内容来源于网络,仅供参考]

In this paper, the formula of Expect Bayes estimation of the reliability under entropy loss function and square loss function for geometric distribution have been given, when the prior distribution of the reliability is power distribution.

本文在几何分布的先验分布为幂分布时研究了其在熵损失函数下,可靠度的多层Bayes估计及其容许性,给出了可靠度的多层Bayes估计的计算公式。

Finishing calculation of mean value, standard deviation, skewness, kurtosis of Beta distribution.(2) Fitting parameters of many kinds of typical distribution and using residual deviation to evaluate fitting precision.(3) Using Beta distribution as an agreed indication distribution applied to many kinds of practical photoelectric measurement distributions.(4) Deriving theory formula of Bayes point estimation about Beta distribution parameters and mean value and standard deviation on the condition of mean square error loss function and supposed the prior distribution is uniform distribution.(5) Generating MCMC sample from post distribution by the method of Gibbs sample algorithm. Calculating bayes point estimation from sample on the condition of mean square error loss function. Calculating confidence interval by an approximate method to complete interval estimation.

本文的主要工作有:(1)解决了Beta分布参数a和b的精确计算以及均值、标准差、偏度、峰度的计算问题;(2)拟合出10余种典型分布的Beta分布的两个参数,并且采用剩余标准差评价该Beta分布的拟合精度;(3)对多种典型的光学与光电测量系统的测量分布进行了Beta分布统示表示;(4)在假设先验分布为均匀分布前提下,得到参数a和b以及均值μ和标准差σ在均方误差损失函数下的贝叶斯点估计理论计算公式;(5)利用直接抽样的Gibbs抽样算法,从后验分布中产生MCMC样本,从样本直接计算均方误差损失函数下的贝叶斯点估计,并使用一种近似方法计算其置信区间,完成区间估计。

Methods According to the Bayesian conjugate prior distribution principle, the estimating methods of prior parameters of the conjugate beta distribution Be of the binomial distribution was given by writing the SAS programs.

根据贝叶斯共轭先验分布原理,对二项分布的共轭贝塔分布中的α、β两个先验参数的确定方法进行分析比较,编写SAS程序确定先验参数。

A new non-linear quadratic programming Bayesian prestack three-terms inversion method is developed; Firstly, this method is based on Bayesian parameter estimation theory, Gaussian distribution is used for likelihood function and modified Cauchy distribution is used for prior distribution; Secondly, covariance matrix is used to describe the degree of correlation between the parameters; rock physics relations are used to constrain the inversion results; at last, this method is transformed into non-linear quadratic programming problem, and inversion results are acquired under several constraints.

本文提出了一种基于非线性二次规划的叠前三参数反演方法。首先基于贝叶斯参数估计理论,假设似然函数服从高斯分布,并使待反演的参数服从于改进的Cauchy分布,从而提高了反演结果的分辨率;其次用协方差矩阵来描述参数间的相关程度,进一步提高了反演结果的稳定性;最后将问题转化为一个非线性二次规划的求解问题,并在多种约束下得到问题的解。

Based on the concept of the prior distribution, the Gamma distribution was introduced as the prior models of the unknown model parameters.

基于共轭先验理论,提出以伽马分布作为未知参数的先验模型,准确估计参数。

We will demonstrate the specification of reference prior distribution on the random covariance matrix. We will consider the approximate uniform shrinkage prior and approximate Jeffreys' prior. Both are formulated based on the approximated likelihood function. Under generalized linear mixed models, we show that the posterior mode under Jeffrey's prior is asymptotically closer to the REML estimate than the mode under uniform shrinkage prior does.

在广义线性混合模式下,我们证明了在样本数很大时,随机效应之变异数成份的后验众数和REML估计值会近似相等,同时也建立了后验众数和REML估计值的近似关系式,并提出了比较二种后验众数与REML估计值之间远近的准则。

Especially since Lindley and Smith (1972) addressed the thought of hierarchical prior distribution and Han (1997) provided the methods to construct hierarchical prior distribution, more achievements have been made on hierarchical Bayesian methods to be used for solving zero-failure data problem.

所谓λ的多层先验分布的构造方法——减函数法,主要是选取λ的减函数作为λ的第一层先验密度函数的核,第二层先验分布——第一层先验分布中超参数的先验分布取均匀分布。

To the establishment of prior distribution in RGTE, considering that reliability growth test data belongs to a dynamic parent population, and based on the stage reliability growth test data, the dynamic modeling methods for prior distribution establishment are studied.

在可靠性增长试验评定先验分布的确定方面,针对可靠性增长试验数据的动态统计母体特点,基于阶段可靠性增长试验数据,对可靠性增长试验评定中先验分布确定的动态建模方法进行研究。

Aimming at the small sample of strapdown inertial measurement unit successive test data, a random weighting method is used to get the prior distribution function of population's parameter with the known prior information under the situation that the parameters' format of general distribution is unknown.

针对捷联惯性测量组合历次测试数据小样本的特点,在总体分布参数形式未知的情况下,根据已有的先验信息,提出了通过随机加权法,获得捷联惯组历次测试数据总体参数的验前分布。

Then give a conveniency and applied Bayes-information fusion method when prior information comes from different sources, and find the prior distribution and posterior distribution of the failure rate.

然后,在产品具有多源验前信息源的情况下,给出了一种方便实用的Bayes信息融合方法,充分融合各验前信息,得到产品失效率的验前分布及后验分布。

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