基于贝叶斯统计法的垃圾邮件过滤研究

介绍了采用贝叶斯统计法过滤垃圾邮件的算法，通过简单贝叶斯过滤算法的研究，引入了派生的贝叶斯过滤算法——零散二元多项式散列法过滤技术，它能够处理单个的特征词和变异的特征词汇．零散二元多项式散列法和贝叶斯统计法的整合，可以建立一个功能强大的垃圾邮件过滤器．  

异方差且未知情况下两正态总体等均值检验的贝叶斯观点统计量

在两正态总体方差不等且未知的前提下 ,检验它们的均值是否相等 ,用经典方法处理此问题是困难的 .而在贝叶斯学派观点看来此检验与等方差情况下的等均值检验并无本质差异 .本文使用贝叶斯学派方法导出此假设检验的统计量 .  

基于图像局域特性的自适应贝叶斯小波收缩去噪算法

在小波去噪过程中,对小波系数进行统计建模,去噪效果会得到较大的改进.该文在贝叶斯萎缩去噪的基础上,提出了基于图像局域特性自适应阈值去噪算法.实验结果表明:新的算法比传统的算法能更有效地去除噪声,获得更高的PSNR.同时,图像中的边缘也保护得更好.  

Bin-based model construction and analytical strategies for dissecting complex traits with chromosome segment substitution lines

Chromosome segment substitution lines have been created in several experimental models,including many plant and animal species,and are useful tools for the genetic analysis and mapping of complex traits.The traditional t-test is usually applied to identify a quantitative trait locus (QTL) that is contained within a chromosome segment to estimate the QTL’s effect.However,current methods cannot uncover the entire genetic structure of complex traits.For example,current methods cannot distinguish between main effects and epistatic effects.In this paper,a linear epistatic model was constructed to dissect complex traits.First,all the long substituted segments were divided into overlapping small bins,and each small bin was considered a unique independent variable.The genetic model for complex traits was then constructed.When considering all the possible main effects and epistatic effects,the dimensions of the linear model can become extremely high.Therefore,variable selection via stepwise regression (Bin-REG) was proposed for the epistatic QTL analysis in the present study.Furthermore,we tested the feasibility of using the LASSO (least absolute shrinkage and selection operator) algorithm to estimate epistatic effects,examined the fully Bayesian SSVS (stochastic search variable selection) approach,tested the empirical Bayes (E-BAYES) method,and evaluated the penalized likelihood (PENAL) method for mapping epistatic QTLs.Simulation studies suggested that all of the above methods,excluding the LASSO and PENAL approaches,performed satisfactorily.The Bin-REG method appears to outperform all other methods in terms of estimating positions and effects.  

随机信息中系统参数的灰色贝叶斯区间估计研究

在贝叶斯可信区间估计理论基础上,借助于灰色理论研究随机信息下贝叶斯灰色可信区间的估计问题,较传统的方法能够获取更多的有效信息.  

基于贝叶斯方法的出行产生率预测

基于贝叶斯统计方法的理论分析,结合贝叶斯方法在交通参数预测中的既有应用,提出了贝叶斯方法预测国内出行产生率的具体方法.该方法以大规模居民调查历史数据为出行产生率的先验分布,以最近小样本抽样数据作为新样本数据,近期出行产生率则为需要预测的后验分布期望值,在先验样本与新样本均超过30的前提下,将先验样本与新样本的均值与方差视为正态分布无偏估计,进而应用贝叶斯统计方法计算得到近期出行产生率,从而达到小样本抽样调查即可对出行产生率进行及时更新的目的.以实例验证了该方法的可行性,并指出其局限性与适用条件.