Lie symmetries of soliton equations with self-consistent sources via source generation procedure

In this paper, the ‘source generation’ procedure (SGP) proposed by Hu and Wang [X.B. Hu, H.Y. Wang, Construction of dKP and BKP equation with self-consistent sources, Inverse Problems 22 (2006) 1903-1920] is utilized to derive Lie symmetries of bilinear soliton equations with self-consistent sources (SESCS) such as KPESCS, BKPESCS, and differential-difference KPESCS. Furthermore, it is shown that these Lie symmetries constitute generators of the corresponding Lie symmetry algebras.     

Average size of -Selmer groups of elliptic curves, I

In this paper, we study a class of elliptic curves over with -torsion group , and prove that the average order of the -Selmer groups is bounded.

     

状态概率的E-Bayes估计与多层Bayes估计

在文献[1]中提出了参数估计的一种方法--E-Bayes估计并给出了状态概率的E-Bayes估计的定义、E-Bayes估计公式、预测模型及其在证券投资中应用,本文在此基础上将给出状态概率的多层Bayes估计、状态概率的E-Bayes估计的性质--E-Bayes估计,多层Bayes估计的关系.最后,给出模拟算例.     

Bayes分析中多源验前信息融合的ML-II方法

在Bayes小子样理论中,验前分布的获取和表示是一个关键问题.针对工程实践中遇到的验前信息的多源性,给出了一种基于第二类极大似然估计原理(M L-II)的多源异总体验前分布的融合方法,并通过仿真实例证明了该方法的有效性.     

广义变系数模型的Bayesian B样条估计

提出了广义变系数模型函数系数的一种新的估计方法．我们用B样条函数逼近函数系数,不具体选择节点的个数,而是节点个数取均匀的无信息先验,样条函数系数取正态先验,用Bayesian模型平均的方法估计各个函数系数．这种估计方法一个主要特点是允许各个函数系数所需节点个数的后验分布不同,因此允许不同函数系数使用不同的光滑参数．另外,本文还给出了Bayesian B样条估计的计算方法,并通过模拟例子,说明广义变系数模型的函数系数可以由Bayesian B样条估计方法得到很好的估计．     

供应链网络可靠性的多层Bayes估计模型

对供应链网络可靠性进行界定和分析,以节点企业间的协同为基础,得到可靠度计算模型,以此为依据采集实证样本无失效运行的数据.根据供应链网络可靠性统计特性,建立一种多层Bayes估计方法,应用于样本可靠性评估中.在估计失效率的基础上,对供应链网络可靠度进行估计,对仿真结果进行分析,显示多层Bayes估计方法应用效果较好,精确度高,反映了参数不确定性对供应链网络可靠性的影响,能够较好地解决依据无失效数据判定供应链网络可靠性水平的问题.     

基于MCMC方法的ARFIMA模型贝叶斯分析及实证研究

在经济领域中,时间序列具有序列相关和长记忆等特征,用考虑了时间序列短记忆性和长记忆的ARFIMA来模型分析研究经济时间序列有利于提高拟合及预测的精度。近几十年来对ARFIMA模型参数估计和分数差分算子阶数d的研究越来越多,该模型的应用也越来越广泛。基于贝叶斯方法在参数估计中的优越性,本文结合众多应用此方法的文献所得到的后验分布特点,提出了合理的先验分布,考虑到计算难度,采用MCMC方法对模型的参数进行估计,最后应用我国过去几十年的GDP数据进行实证分析,得到了ARFIMA模型参数的后验分布图、均值、方差及95%的置信区间。     

Copula models for insurance claim numbers with excess zeros and time-dependence

This paper develops two copula models for fitting the insurance claim numbers with excess zeros and time-dependence. The joint distribution of the claims in two successive periods is modeled by a copula with discrete or continuous marginal distributions. The first model fits two successive claims by a bivariate copula with discrete marginal distributions. In the second model, a copula is used to model the random effects of the conjoint numbers of successive claims with continuous marginal distributions. Zero-inflated phenomenon is taken into account in the above copula models. The maximum likelihood is applied to estimate the parameters of the discrete copula model. A two-step procedure is proposed to estimate the parameters in the second model, with the first step to estimate the marginals, followed by the second step to estimate the unobserved random effect variables and the copula parameter. Simulations are performed to assess the proposed models and methodologies.     

用贝叶斯公式解释化验结果

借助概率论中的贝叶斯公式理论和方法,对现实中人们对有关化验结果的疑惑进行了详细的解释,从而使人们能更科学地理解化验结果,深刻感知数学在解决实际问题的作用.     

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??The Bayesian model are established for the VaR and related risk measurements. The relationship between VaR and other risk measurements including expect shortfall, tail condition expectation and conditional value at risk are discussed. Furthermore, the Bayesian estimates and Bayesian predictors of these risk measurement are derived. Thirdly, the consistency and asymptotic normality in the exponential risk model are proved. Finally, the numerical simulation method is used to verify the convergence rate under different sample sizes.