基于后验风险确定故障样本量的Bayes方法

针对目前测试性故障样本量的确定方法过于粗糙和试验样本量过大的问题,提出了合理运用信息熵方法对装备系统各单元的测试性先验信息进行信息融合,得到装备系统级测试性试验数据。在此基础上得到测试性指标的先验分布,并进一步通过Bayes后验风险准则确定故障样本量及试验方案。以某型电动舵机系统各模块的试验数据为例,以故障检测率为测试性指标,经过分析和计算,发现运用所提方法得到的故障样本量相比传统方法明显减少,从而减少了试验成本,同时得到的测试性指标相对误差较小,保证了可信度。     

Subset threshold autoregression

We develop in this paper an efficient way to select the best subset threshold autoregressive model. The proposed method uses a stochastic search idea. Differing from most conventional approaches, our method does not require us to fix the delay or the threshold parameters in advance. By adopting the Markov chain Monte Carlo techniques, we can identify the best subset model from a very large of number of possible models, and at the same time estimate the unknown parameters. A simulation experiment shows that the method is very effective. In its application to the US unemployment rate, the stochastic search method successfully selects lag one as the time delay and five best models from more than 4000 choices. Copyright © 2003 John Wiley & Sons, Ltd.     

三参数Weibull分布下随机截尾恒加寿命试验的Bayes统计分析

利用随机截尾恒加寿命试验所获的数据，导出了三参数Ｗｅｉｂｕｌ分布在平方损失下，三个参数的Ｂａｙｅｓ估计，进一步估计了在正常应力水平下的各种可靠性指标     

双参数情形的经验Bayes估计近似算法

该文给出了双参数情形利用线性经验Ｂａｙｅｓ估计公式估计待估参数的近似算法，并以正态分布为情形给出具体的计算形式．     

广义离散随机线性系统的状态估计新方法

运用Ｂａｙｅｓ动态模型理论［２］讨论了广义离散随机线性系统的状态估计问题，提出了一种既简便又实用的状态估计新方法──线性贝叶斯估计（ＬＢＥ）．     

LINEX损失下一类分布族参数渐近最优的EB检验

目的 在LINEX损失函数下，讨论一类双边截断型分布族参数的经验Bayes(EB)检验问题。方法 构造适当的EB判决(检验)函数。结果 在经验Bayes检验问题中，将损失函数推广为LINEX损失，在适当的条件下证明了所构造的判决函数是渐近最优的。结论 LINEX损失函数具有比对称损失更广泛的意义，而且在一定条件下可以获得参数渐近最优的EB检验。     

Logistic回归模型的Bayes分析

本文应用Bayes观点讨论了logistic模型中的参数估计问题。     

Induction of hybrid decision tree based on post-discretization strategy

By redefining test selection measure, we propose in this paper a new algorithm, Flexible NBTree, which induces a hybrid of decision tree and Naive Bayes. Flexible NBTree mitigates the negative effect of information loss on test selection by applying postdiscretization strategy: at each internal node in the tree, we first select the test which is the most useful for improving classification accuracy, then apply discretization of continuous tests. The finial decision tree nodes contain univariate splits as regular decision trees, but the leaves contain Naive Bayesian classifiers. To evaluate the performance of Flexible NBTree, we compare it with NBTree and C4.5, both applying pre-discretization of continuous attributes. Experimental results on a variety of natural domains indicate that the classification accuracy of Flexible NBTree is substantially improved.     

基于Bayes网络的项目风险评估方法研究

本文研究了项目风险评估的建模方法 ,通过与其它传统的项目风险评估方法比较 ,针对项目风险评估的信息不完备、具有一定不确定性的特点 ,选择Bayes网络建立项目风险评估模型。本文内容适合于在不确定环境下 ,信息不完备时的项目风险评估