Approximate Bayesian Approach to Non-Gaussian Estimation in a Linear Model with Dependent State and Noise Vectors

This paper extends the results of Masreliez [8] on the design of non-Gaussian estimators for a more general class of the parameter estimation problem when the system state and the observation noise may be dependent and non-Gaussian simultaneously. It is shown that the proposed non-Gaussian algorithms can approximate with high precision the minimum mean square estimator. Application of the approach to the design of different optimal (and stable) estimation algorithms is illustrated. The efficiency of the proposed algorithms is tested in some simulation experiments. Accepted 5 September 2000. Online publication 26 February 2001.     

非线性Bayes动态模型预测的数值算法

本文用一阶样条函数分段逼近密度函数的方法给出了非线性 Bayes动态模型预测的一个数值算法     

两种抽样方案的最优化设计

本文运用贝叶斯分析方法导出两类抽样方案中样本容量ｎ和合格判定数ｃ之间的最优关系,从而得到两种抽样方案的最优化设计方法     

压缩真空光输入和光子计数法增强Sagnac效应

压缩真空光输入和平衡零拍探测可有效增强Sagnac效应,提高陀螺精度;考虑平衡零拍探测的相位精度与相位自身相关,仅在某特定相位能达到最佳灵敏度,设计了一种基于光子计数法提取Sagnac输出相位的方案,并利用贝叶斯理论估计相位.理论分析结果表明,该方法能突破散粒噪声极限,相位精度不再受限于相位自身,且当压缩真空光和相干激光功率相同时,精度在理论上能达到海森堡极限.     

基于贝叶斯神经网络的近红外光谱实木地板表面缺陷检测

实木地板的表面缺陷直接影响其力学性能和产品等级,表面缺陷的快速检测对实木地板的在线分选具有重要的现实意义。针对视觉方法检测实木地板表面缺陷识别率低的问题,提出了一种基于近红外光谱分析技术的检测方法。首先,分别采集规格为200 mm×100 mm×20 mm的表面带有活节、死节以及无缺陷的实木地板的光谱数据各60份,其中30份作为训练样本,30份作为测试样本;其次,使用高斯滤波(GSF)、分段多元散射校正(PMSC)和去趋势法(DT)等方法对采集到的光谱数据进行预处理,降低光谱噪声、消除光谱的散射影响;然后,利用改进遗传算法从处理后的光谱中提取特征波长用于构建缺陷识别与分类模型;最后,使用基于贝叶斯理论改进的神经网络构建实木地板缺陷识别和分类模型。实验使用含有活节、死节以及无缺陷的实木地板样本对模型进行训练和测试,结果表明：通过贝叶斯神经网络构建的缺陷识别与分类模型能够准确识别活节、死节和无缺陷三类实木地板,识别率分别为92.20%, 94.47%和95.57%。证明了实木地板表面缺陷类型与其近红外吸收光谱密切相关,并为下一步实现实木地板表面缺陷的准确定位提供快速检测方法。     

Guided filter and convolutional network based tracking for infrared dim moving target

The dim moving target usually submerges in strong noise, and its motion observability is debased by numerous false alarms for low signal-to-noise ratio. A tracking algorithm that integrates the Guided Image Filter (GIF) and the Convolutional neural network (CNN) into the particle filter framework is presented to cope with the uncertainty of dim targets. First, the initial target template is treated as a guidance to filter incoming templates depending on similarities between the guidance and candidate templates. The GIF algorithm utilizes the structure in the guidance and performs as an edge-preserving smoothing operator. Therefore, the guidance helps to preserve the detail of valuable templates and makes inaccurate ones blurry, alleviating the tracking deviation effectively. Besides, the two-layer CNN method is adopted to obtain a powerful appearance representation. Subsequently, a Bayesian classifier is trained with these discriminative yet strong features. Moreover, an adaptive learning factor is introduced to prevent the update of classifier’s parameters when a target undergoes sever background. At last, classifier responses of particles are utilized to generate particle importance weights and a re-sample procedure preserves samples according to the weight. In the predication stage, a 2-order transition model considers the target velocity to estimate current position. Experimental results demonstrate that the presented algorithm outperforms several relative algorithms in the accuracy.     

Bayesian compressive sensing for thermal imagery using Gaussian-Jeffreys prior

Recent advances have shown a great potential to explore compressive sensing (CS) theory for thermal imaging due to the capability of recovering high-resolution information from low-resolution measurements. In this paper, we present a Bayesian CS reconstruction algorithm that makes use of a new sparsity-inducing prior, referred as Gaussian-Jeffreys prior, and demonstrate performance gain of imposing this new prior on thermal imagery where the signal-to-noise ratio is low. We first derive a hierarchical representation of the Gaussian-Jeffreys prior that facilitates computational tractability, then propose an efficient evidence approximation inference algorithm. We show that the proposed estimator is able to provide stronger sparsity-inducing power comparing to the conventional choices. Extensive numerical examples are provided with performance comparisons of different CS estimators, in particular when the compressive measurements are available via thermal imaging.     

A Case Study of Stochastic Optimization in Health Policy: Problem Formulation and Preliminary Results

We use Bayesian decision theory to address a variable selection problem arising in attempts to indirectly measure the quality of hospital care, by comparing observed mortality rates to expected values based on patient sickness at admission. Our method weighs data collection costs against predictive accuracy to find an optimal subset of the available admission sickness variables. The approach involves maximizing expected utility across possible subsets, using Monte Carlo methods based on random division of the available data into N modeling and validation splits to approximate the expectation. After exploring the geometry of the solution space, we compare a variety of stochastic optimization methods –- including genetic algorithms (GA), simulated annealing (SA), tabu search (TS), threshold acceptance (TA), and messy simulated annealing (MSA) –- on their performance in finding good subsets of variables, and we clarify the role of N in the optimization. Preliminary results indicate that TS is somewhat better than TA and SA in this problem, with MSA and GA well behind the other three methods. Sensitivity analysis reveals broad stability of our conclusions.     

用Gibbs抽样算法计算定数截尾时Weibull分布的贝叶斯估计

本文假设产品寿命服从两参数 Weibull分布 ,在定数截尾寿命试验的情况下 ,利用近年来发展起来的 Gibbs抽样算法 ,设计了计算参数贝叶斯估计的 Gibbs抽样方案 ,计算了一个实例并与经典的 BL UE和 BL IE估计结果进行比较。结果表明 ,较之传统的数值积分法 ,Gibbs抽样算法使用起来方便直接 ,更适合于计算可靠性指标的贝叶斯估计     

无失效数据情形失效率的估计及其应用

该文对指数分布的无失效数据,在失效率　的先验密度的核为　ａ－１时,给出了失效率　的Ｂａｙｅｓ估计和多层Ｂａｙｅｓ估计．并对某液压电动机,在寿命眼从指数分布时,给出了该液压电动机无失效数据情形可靠度的估计．