Estimation for partially observed Markov processes

Many stochastic process models for environmental data sets assume a process of relatively simple structure which is in some sense partially observed. That is, there is an underlying process (X_n, n 0) or (X_t, t 0) for which the parameters are of interest and physically meaningful, and an observable process (Y_n, n 0) or (Y_t, t 0) which depends on the X process but not otherwise on those parameters. Examples are wide ranging: the Y process may be the X process with missing observations; the Y process may be the X process observed with a noise component; the X process might constitute a random environment for the Y process, as with hidden Markov models; the Y process might be a lower dimensional function or reduction of the X process. In principle, maximum likelihood estimation for the X process parameters can be carried out by some form of the EM algorithm applied to the Y process data. In the paper we review some current methods for exact and approximate maximum likelihood estimation. We illustrate some of the issues by considering how to estimate the parameters of a stochastic Nash cascade model for runoff. In the case of k reservoirs, the outputs of these reservoirs form a k dimensional vector Markov process, of which only the kth coordinate process is observed, usually at a discrete sample of time points.     

基于可分解马尔科夫网的极端椒盐噪声图像滤波

提出了一种基于可分解马尔科夫网(decomposableMarkovnetworks,DMN)的极端椒盐噪声的均值滤波方法,指出其网络节点的阈值衰减特性和网络节点的连接特性具有很好的对椒盐噪声污染图像的噪声定位的作用,并提出一种在该网络控制下,只对与噪声相关的像素进行均值计算以替代噪声像素的亚均值滤波算法,实现了图像的较强自适应滤波。实验表明,本文方法具有良好的滤波性能。     

基于改进Markov-CA模型的黄土高原土地利用多情景模拟

土地利用/覆被的时空变化研究能为区域生态环境恢复和生态系统集成管理提供科学支持.集成Logistic回归模型、改进的Markov与FLUS模型模拟黄土高原2020-2050年3种典型情景土地利用变化.发现各情景土地利用面积变化及空间置换转移主要集中在农用地、草地和城镇用地;历史趋势延续情景下农用地减少15 205 km...     

摄动理论在腐蚀管线随机地震反应分析中的应用

埋地管线的腐蚀情况十分复杂,具有随机性的特点,从而导致其在地震激励下的反应必然具有随机性。本文在管线腐蚀离散状态模型和弹性地基梁原理的基础上,利用随机摄动理论推导给出了腐蚀管线在地震激励下位移和应力的解析表达式,并进行了均值和标准差的计算。实例分析中将该方法计算结果与Monte Carlo模拟方法计算结果进行了对比。结果表明,利用随机摄动方法可以快速、精确地求得腐蚀管线的随机地震反应。     

有限m重非齐次马氏链随机变换的一个强极限定理

将赌博系统的随机变换概念推广到有限优重非齐次马氏链情形,采用鞅方法研究了有限m重非齐次马氏链函数的强极限定理,作为推论得到有限m重非齐次马氏链随机变换的若干强极限定理。     

The decision of the optimal parameters in Markov random fields of images by genetic algorithm

This paper introduces the principle of genetic algorithm and the basic method of solving Markov random field parameters. Focusing on the shortcomings in present methods, a new method based on genetic algorithms is proposed to solve the parameters in the Markov random field. The detailed procedure is discussed. On the basis of the parameters solved by genetic algorithms, some experiments on classification of aerial images are given. Experimental results show that the proposed method is effective and the classification results are satisfactory.     

Estimation for partially observed Markov processes

Many stochastic process models for environmental data sets assume a process of relatively simple structure which is in some sense partially observed. That is, there is an underlying process (X_n, n 0) or (X_t, t 0) for which the parameters are of interest and physically meaningful, and an observable process (Y_n, n 0) or (Y_t, t 0) which depends on the X process but not otherwise on those parameters. Examples are wide ranging: the Y process may be the X process with missing observations; the Y process may be the X process observed with a noise component; the X process might constitute a random environment for the Y process, as with hidden Markov models; the Y process might be a lower dimensional function or reduction of the X process. In principle, maximum likelihood estimation for the X process parameters can be carried out by some form of the EM algorithm applied to the Y process data. In the paper we review some current methods for exact and approximate maximum likelihood estimation. We illustrate some of the issues by considering how to estimate the parameters of a stochastic Nash cascade model for runoff. In the case of k reservoirs, the outputs of these reservoirs form a k dimensional vector Markov process, of which only the kth coordinate process is observed, usually at a discrete sample of time points.     

基于高斯过程的高分辨率遥感图像变化检测

本文首先通过理论分析,探讨了高斯过程分类器在高分辨率遥感图像变化检测应用中的优势与不足,并针对高斯过程分类器的不足给出了相应的解决方法;其次,提出了一种基于空间上下文相关的高斯过程变化检测方法;最后,通过多个高分辨率遥感实验数据集上的实验设计与分析,验证了高斯过程分类器在高分辨率遥感图像变化检测中的应用能力,并证明了本文提出的解决方法的有效性.     

基于遗传算法的影像纹理分类

阐述了遗传算法的基本原理,提出了与马尔柯夫随机场相结合的遗传算法在影像纹理分类中的应用。通过对４种纹理取样的实验,表明这种影像纹理分类方法可以提高分类的正确率。     

基于Markov链模型的储层岩相随机模拟

在油气储层随机建模研究中，基于Markov链模型的方法是一类较受欢迎的技术，同时也是一类不成熟的技术，问题的症结之一在于侧向的转移概率矩阵很难求取，针对这种情况，作者在深入理解Walther相律的基础上，借鉴模拟退火算法的相应思路，提出了一种岩相模拟的新方法，该方法依据不同岩相的百分比进行随机模拟得到一幅初始图象，而后以按岩相组织剖面得到的垂向和侧向的岩相转移概率矩阵的相似性作为判别标准对图象进行扰动，直至得到满意的图象，二维模型试算结果表明了这种岩相随机模拟方法的可行性。