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在单参数指数族下,对参数的双侧检验问题给出了一致最优检验或一致最优无偏检验的p-值;在多参数指数族下,对单个参数双侧检验问题给出了一致最优无偏检验的p-值.在正态总体下,给出了几个计算上述p-值的例子. 相似文献
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在单参数模糊微分方程基础上研究了一阶多参数模糊微分方程和模糊初值问题,利用刻画方程的解与刻画参数的关系给出了多参数模糊微分方程解存在的条件,最后给出了具体算例.表明,多参数模糊微分方程具有广泛的工程应用背景. 相似文献
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韩明 《纯粹数学与应用数学》2016,32(3):235-242
给出了参数的E-Bayes估计的定义,对Pareto分布在尺度参数已知时,在平方损失下给出了形状参数的E-Bayes估计和多层Bayes估计,并且用Monte Carlo方法给出了模拟算例.最后,结合高尔夫球手收入数据的实际问题进行了计算,结果表明本文提出的方法可行且便于应用. 相似文献
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龙兵 《数学的实践与认识》2013,43(7):104-109
首先给出了艾拉姆咖分布在定数截尾场合下参数的极大似然估计;其次由"平均剩余寿命"的概念得到了参数的拟矩估计;然后取共轭先验分布给出了参数的经验Bayes估计、区间估计及假设检验;最后通过实例给出了不同截尾样本下参数的点估计和区间估计. 相似文献
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研究发汗冷却控制系统中气动加热热流密度的参数辨识问题.证明了该参数辨识的存在及唯一性,给出了参数辨识所满足的充分必要条件,最后,根据得到的充分必要条件,尝试直接构造极小化序列,进而给出该系统参数辨识的算法. 相似文献
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为了使得随机积分水平集算法中的积分水平值能够更加有效地下降,使每次下降得到的参数更适应目标函数,本文将相对熵方法应用到随机积分水平集算法中来.利用相对熵中的ASP问题给出了一种新的参数更新方法,数值试验证明了其科学性.最后就该方法给出了更加一般的参数更新方法并给出了算法. 相似文献
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在实际应用中,两参数Gumbel分布的贝叶斯估计往往需要预先知道Gumbel参数的二维联合先验分布。由于获取先验分布的主观性和统计推断的复杂性,目前有关Gumbel分布贝叶斯估计理论及其性质的讨论还比较少,更不要说获得较为简单的Gumbel分布的贝叶斯估计。本文基于Kaminskiy和Vasiliy提出的简单贝叶斯估计过程,利用可靠度函数估计的区间形式表示先验信息,从而得到两个参数Gumbel分布的简单贝叶斯估计。基于此先验信息,该估计过程构造了Gumbel参数的连续联合先验分布,给出了在给定任意时点的可靠度(或累积密度)及其标准差的后验估计,为可靠性与风险评估中简单快速的使用贝叶斯估计刻画极端事件提供了可能. 相似文献
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We consider Bayesian shrinkage predictions for the Normal regression problem under the frequentist Kullback-Leibler risk function.Firstly, we consider the multivariate Normal model with an unknown mean and a known covariance. While the unknown mean is fixed, the covariance of future samples can be different from that of training samples. We show that the Bayesian predictive distribution based on the uniform prior is dominated by that based on a class of priors if the prior distributions for the covariance and future covariance matrices are rotation invariant.Then, we consider a class of priors for the mean parameters depending on the future covariance matrix. With such a prior, we can construct a Bayesian predictive distribution dominating that based on the uniform prior.Lastly, applying this result to the prediction of response variables in the Normal linear regression model, we show that there exists a Bayesian predictive distribution dominating that based on the uniform prior. Minimaxity of these Bayesian predictions follows from these results. 相似文献
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无失效数据情形参数的综合估计 总被引:2,自引:0,他引:2
本对指数分布的无失效数据,在引进失效信息后,在先验分布为Gamma分布时,给出了失效率的多层Bayes估计和综合Bayes估计,并给出了无失效数据情形可靠度的综合估计,还结合实际问题进行了计算。 相似文献
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Haohai Yu 《International Journal of Approximate Reasoning》2010,51(7):800-819
Approximate Bayesian inference by importance sampling derives probabilistic statements from a Bayesian network, an essential part of evidential reasoning with the network and an important aspect of many Bayesian methods. A critical problem in importance sampling on Bayesian networks is the selection of a good importance function to sample a network’s prior and posterior probability distribution. The initially optimal importance functions eventually start deviating from the optimal function when sampling a network’s posterior distribution given evidence, even when adaptive methods are used that adjust an importance function to the evidence by learning. In this article we propose a new family of Refractor Importance Sampling (RIS) algorithms for adaptive importance sampling under evidential reasoning. RIS applies “arc refractors” to a Bayesian network by adding new arcs and refining the conditional probability tables. The goal of RIS is to optimize the importance function for the posterior distribution and reduce the error variance of sampling. Our experimental results show a significant improvement of RIS over state-of-the-art adaptive importance sampling algorithms. 相似文献
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无失效数据失效率的综合多层Bayes估计 总被引:5,自引:1,他引:4
文章对指数分布无失效数据的失效率,在先验分布为Gamma分布时,在引进失效信息后,给出了多层Bayes估计以及综合多层Bayes估计,并给出了可靠度的综合估计。最后,结合实际问题进行了计算 相似文献
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参数的E Bayes估计法及其应用 总被引:6,自引:0,他引:6
韩明 《数学的实践与认识》2004,34(9):97-106
提出了参数的一种估计方法—— E Bayes估计法 ,对寿命服从指数分布的产品 ,在失效率的先验分布为 Gamma分布时 ,给出了失效率的 E Bayes估计和多层 Bayes估计 ,并在此基础上给出了失效率和可靠度的 E Bayes估计的性质 .结合实际问题进行了计算 ,结果表明提出的 E Bayes估计法可行且便于应用 . 相似文献
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《International Journal of Approximate Reasoning》2014,55(7):1548-1569
This paper considers the problem of learning multinomial distributions from a sample of independent observations. The Bayesian approach usually assumes a prior Dirichlet distribution about the probabilities of the different possible values. However, there is no consensus on the parameters of this Dirichlet distribution. Here, it will be shown that this is not a simple problem, providing examples in which different selection criteria are reasonable. To solve it the Imprecise Dirichlet Model (IDM) was introduced. But this model has important drawbacks, as the problems associated to learning from indirect observations. As an alternative approach, the Imprecise Sample Size Dirichlet Model (ISSDM) is introduced and its properties are studied. The prior distribution over the parameters of a multinomial distribution is the basis to learn Bayesian networks using Bayesian scores. Here, we will show that the ISSDM can be used to learn imprecise Bayesian networks, also called credal networks when all the distributions share a common graphical structure. Some experiments are reported on the use of the ISSDM to learn the structure of a graphical model and to build supervised classifiers. 相似文献
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无失效数据情形失效率的综合估计 总被引:4,自引:0,他引:4
韩明 《高校应用数学学报(A辑)》2002,17(2):200-206
对指数分布的无失效数据,提出了无失效数据情形失效率的综合估计法。在失效率的先验分布为截尾Gamma分布时,给出了失效率的多层Bayes估计。在引进失效信息后,在失效率的先验分布为截尾Gamma分布时,给出了失效率的多层Bayes估计和综合估计,并给出了可靠度的综合估计,结合实际问题进行了计算。 相似文献
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