Non-parametric empirical Bayes procedure

In this paper we introduce an empirical Bayes procedure for estimating an unknown parameter, say θ. This procedure gives the empirical Bayes estimator for θ and its associated minimum posterior risk in closed forms without estimating the unknown prior density function of θ. In such procedure the posterior probability density function of θ is not required. A sufficient statistic for θ with conditional probability density function in the one parameter exponential family is required. Instead of estimating the unknown prior density function, the marginal density function of the sufficient statistic must be estimated. As special cases the empirical Bayes estimators and their respective minimum posterior risks of the failure rate for the exponential distribution, the unknown scale parameters of Weibull and gamma distributions are obtained in simple forms as special cases. Numerical results and a simulation study are introduced to (i) investigate how the number of available past experiments and the sample size of each influence the accuracy of the empirical Bayes estimator, (ii) make a comparison between the presented procedure and the Bayes procedure when the prior probability density function of the parameter θ is gamma.     

Case Based Imprecision Estimates for Bayes Classifiers with the Bayesian Bootstrap

This article outlines a Bayesian bootstrap method for case based imprecision estimates in Bayes classification. We argue that this approach is an important complement to methods such as k-fold cross validation that are based on overall error rates. It is shown how case based imprecision estimates may be used to improve Bayes classifiers under asymmetrical loss functions. In addition, other approaches to making use of case based imprecision estimates are discussed and illustrated on two real world data sets. Contrary to the common assumption, Bayesian bootstrap simulations indicate that the uncertainty associated with the output of a Bayes classifier is often far from normally distributed.     

非均匀噪声图像的小波去噪

目前的小波阈值去噪一般考虑单幅均匀噪声样本，针对非均匀噪声模型，提出基于邻域估计法的逐点阈值去噪算法；并将多幅均匀噪声样本的联合去噪法推广到非均匀噪声模型下。其中。阈值函数的选取是：基于信号方差和噪声方差的自适应的逐噗Bayes阈值；对多幅非均匀噪声样本采用阈值去噪与加权平均结合的方案，加权系数由各象素点的噪声方差确定，并比较了图像域和变换域两种加权方法的性能。     

多智能方法及其融合系统在化工过程故障诊断中的应用

简要介绍了决策树、神经网络、K近邻等五种智能方法,提出了一种将这五种智能方法相融合的故障诊断系统,并将其应用在催化裂化装置上.通过对这五种智能方法及其融合系统的性能考察表明,融合策略发挥了各方法的优点,回避了它们的缺点,提升了系统的整体性能,其召回率为100%,精确度为98%.     

基于贝叶斯动态模型的某器件性能预测

在导弹武器系统当中，及时准确的故障预报对提高导弹的安全性具有极其重要的意义。本文根据导弹惯性器件故障预报系统的设计要求，利用贝叶斯动态线性模型对导弹某惯性器件的性能进行了预测研究。贝叶斯预测是利用历史信息和从样本获得的信息来获取后验分布的一种预测方法，该方法不需要平稳性的假设，而且充分利用了已有的信息，可以用来对电子设备的性能进行预测。实例研究表明，该方法具有比较好的预测效果。     

Data decomposition using independent component analysis with prior constraints

In many data analysis problems, it is useful to consider the data as generated from a set of unknown (latent) generators or sources. The observations we make of a system are then taken to be related to these sources through some unknown function. Furthermore, the (unknown) number of underlying latent sources may be less than the number of observations. Recent developments in independent component analysis (ICA) have shown that, in the case where the unknown function linking sources to observations is linear, such data decomposition may be achieved in a mathematically elegant manner. In this paper, we extend the general ICA paradigm to include a very flexible source model, prior constraints and conditioning on sets of intermediate variables so that ICA forms one part of a hierarchical system. We show that such an approach allows for efficient discovery of hidden representation in data and for unsupervised data partitioning.     

分类和预测问题中名词型属性的一种预处理方法

在分类和预测任务中，包含大量不同取值的名词型属性使那些要求数值型输入的回归算法难以使用。该文提出一种基于经验贝叶斯统计方法的预处理方法，对这一类名词型属性进行变换，使之能用于预测建模。首先介绍了变换的统计学原理，然后给出了实现方法。分析表明，此方法简单易行，缩放性较好，而且在处理缺失数据时具有明显的优势。     

基于蚁群算法的容迟网络概率路由算法

容迟网络的概率路由算法根据节点与信宿节点之间相遇的概率判断是否转发报文,但相遇概率不能真正准确地反映报文递交成功的概率.针对该问题,提出一种基于蚁群算法的概率路由算法,结合蚁群算法的基本原理改进信息素浓度的更新机制和报文转发机制.仿真结果表明,与传统的概率路由算法相比,改进后的概率路由算法报文递交率较高,开销率较小.     

基于加速度的小波能量特征及样本熵组合的步态分类算法

摘 要： 目的 针对传统的使用小波分解系数作为特征对走路、上楼、下楼进行分类时不能对具有相同强度加速度信号的步态样本进行分类的问题,提出了一种基于样本熵和小波能量相结合作为特征的分类算法。方法 利用三轴加速度传感器采集走路、上楼、下楼三种步态下的上臂加速度信号,将信号进行小波分解,提取能量特征和样本熵特征,构建决策树分类器和贝叶斯分类器。结果 决策树分类器和贝叶斯分类器的总体分类精度分别为75%和78.75%,使用样本熵与小波能量作为特征的分类精度比仅使用小波能量的分类精度提高了15.85%和19.17%。结论 就步态分类精度而言,样本熵与小波能量相结合的方法优于仅使用传统小波能量方法。