Bootstrapping a Bayes estimator of a survival function with censored data

The large-sample frequentist property of a frequentist bootstrap for a posterior mean with respect to a Dirichlet prior of the survival function for a randomly censored data is given. The weak convergence of a bootstrap version of the Susarla-Van Ryzin estimator is established on the whole real line. An illustration of the technique and some Monte Carlo studies are also given.     

Compressed Multi-image Reconstruction Based on Quantization Noise Distribution

Because of the quantization noise introducedduring the compression, super-resolution reconstruction(SRR) techniques are complicated for the compressedimages. This paper aims to incorporate the prior knowledgeof discrete cosine transform(DCT) coefficients intomodeling the quantization noise. The spatial covariancematrix of the quantization noise is estimated by utilizing theLaplacian distribution of the alternating current(AC)coefficients. After estimating the spatial joint covariance ofoverall noises for the imaging system, we propose a generalBayesian framework to enhance the resolution forcompressed images. Experiments demonstrate theeffectiveness of the proposed algorithm and show thesuperiority to previous methods in objective and subjectiveaspects.     

计算机辅助编程教学专家系统的设计与建造

本文介绍了一个可以帮助学生排除程序设计中的常见错误，并适用于计算机语言教学的辅助编程教学专家系统．     

On the consistent inference of probability distributions

We respond to criticism (Karbelkar 1986) concerning our approach to consistent inference of probabilities for reproducible experiments (Tikochinskyet al 1984).     

Smoothness Properties and Gradient Analysis Under Spatial Dirichlet Process Models

When analyzing point-referenced spatial data, interest will be in the first order or global behavior of associated surfaces. However, in order to better understand these surfaces, we may also be interested in second order or local behavior, e.g., in the rate of change of a spatial surface at a given location in a given direction. In a Bayesian parametric setting, such smoothness analysis has been pursued by Banerjee and Gelfand (2003) and Banerjee et al. (2003). We study continuity and differentiability of random surfaces in the Bayesian nonparametric setting proposed by Gelfand et al. (2005), which is based on the formulation of a spatial Dirichlet process (SDP). We provide conditions under which the random surfaces sampled from a SDP are smooth. We also obtain complete distributional theory for the directional finite difference and derivative processes associated with those random surfaces. We present inference under a Bayesian framework and illustrate our methodology with a simulated dataset.     

均值已知的条件下方差的统计推断

充分利用总体的信息，讨论了正态总体均值μ已知的条件下，方差σ^2的统计推断问题．     

计算机安全学研究

提出计算机安全学的概念。讨论作为安全学基础的存取、隔离、加密、信息流向和推断控制原理,也论及安全学的应用问题。     

可靠性统计分析中“倒挂”数据的处理方法

在贮存可靠性中,经常会出现“倒挂”数据.本文讨论了“倒挂”数据的处理方法.在合适的数学模型下,用Bayes方法得到了一些有用的结果.     

内燃机油基础油与添加剂优选中的推理方法

为了研制内燃机润滑油，采用计算机知识推理的方法来选择基础油和添加剂。知识的表示以传统的基于规则的知识表示为基础，结合面向对象的以及模糊的知识表示方法，为每种添加剂建立规则库。以添加剂类型规则库中的规则头以及研制油的性能要求分别得到两个证据集合，以两集合的贴近库作为其匹配度，从而选择出符合要求的添加剂类型。以该类型添加剂中的每种添加剂性能指标形成的评判标准论域集合与选择该添加剂的模糊评判论域集合为基础，应用麦德姆尼方法推出模糊关系矩阵，用研制油的评判集合及模糊假言推理得到结论，在多个结论的情况下，对结论冲突进行消解，最终选择出最优的添加剂。实践表明，这种推理方法得到的添加剂与大量人工分析得到的结论完全相同。     

Duane-LR模型下复杂系统的动态可靠性增长评定

基于复杂系统可靠性增长试验的特点，运用Duane可靠性增长模型结合数理统计中的线性回归方法对新批次产品的可靠性参数进行预测。结合产品的少量现场试验数据，利用Bayes方法对系统的可靠性增长试验结果进行评定。文中首先给出了可靠性增长分析的模型，然后运用历次阶段试验中的可靠性增长数据建立动态参数的递推估计模型，在此基础上，运用随机变量函数的分布，给出各阶段可靠性增长试验中可靠性参数的Bayes估计。文中对Weibull、指数和二项分布三种试验结果进行分析，给出计算公式。