Maximum Likelihood Estimation Methods for Copula Models

Computational Economics - For Copula models, the likelihood function could be multi-modal, and some traditional optimization algorithms such as simulated annealing (SA) may get stuck in the local...     

动态模糊极大似然估计算法

在机器学习中,一个广泛的应用是对模型的参数进行估计,即极大似然估计(MLE),EM算法是根据点估计中的MLE改进的一种迭代算法,是求极大似然估计的一种强有力的工具,但它收敛速度较慢,于是引入α-EM算法,克服了EM算法的缺陷.由于学习的过程中可能存在着大量的缺失数据及其动态模糊性,给出基于不完全数据的动态模糊极大似然估计算法并给出实例验证.     

基于最大似然估计的自适应图像降噪

应用Stein的无偏风险估计改进Mih？ak等提出的LAWML小波域图像降噪算法。该方法能在每一个子带为LAWML方法确定一个最佳的邻域窗口,也将建议的方法推广到对偶树复数小波变换。实验结果证实,该方法不仅优于LAWML,也优于当前其他一些图像降噪算法。     

Maximum Likelihood Estimation of State Space Models From Frequency Domain Data

This paper addresses the problem of estimating linear time invariant models from observed frequency domain data. Here an emphasis is placed on deriving numerically robust and efficient methods that can reliably deal with high order models over wide bandwidths. This involves a novel application of the expectation-maximization algorithm in order to find maximum likelihood estimates of state space structures. An empirical study using both simulated and real measurement data is presented to illustrate the efficacy of the solutions derived here.     

Gradient Parameter Estimation of a Class of Nonlinear Systems Based on the Maximum Likelihood Principle

International Journal of Control, Automation and Systems - This paper studies the maximum likelihood identification problems of the bilinear-in-parameter output-error systems with colored noise. A...     

图像高斯噪声估计的极大似然算法

提出一种图像高斯噪声极大似然估计方法,目的是估计出噪声图像所含噪声大小。首先,根据高斯噪声模型的特点,用极大似然法估计噪声值,对图像所含噪声模型进行分析。其次,把噪声图像用直方图表示,从归一化直方图中选出不同的样本观测值,用极大似然算法对噪声的方差进行估计。最后,用MATLAB对该方法进行了模拟实验,实验结果表明此方法所得的图像噪声的方差与实际图像噪声的方差近似相等。所以,此方法无论是在准确性上还是在可行性上均具有优良的特性。     

Maximum Likelihood Least Squares Based Iterative Estimation for a Class of Bilinear Systems Using the Data Filtering Technique

International Journal of Control, Automation and Systems - Maximum likelihood methods are based on the probability and statistics theory, and significant for parameter estimation and system...     

Maximum Likelihood Estimation for the Asymmetric Exponential Power Distribution

Computational Economics - The asymmetric exponential power (AEP) distribution has received much attention in economics and finance. Simulation study shows that iterative methods developed for...     

Maximum Likelihood Robust Regression by Mixture Models

In this paper, we will consider a robust estimator, which was proposed earlier by the authors, in a general non-linear regression framework. The basic idea of the estimator is, instead of trying to classify the observations to good and false, to model the residual distribution of the contaminants, determine the probability for each observation to be a good sample, and finally perform weighted fitting. The main contributions of this paper are: (1) We show now that the estimator is consistent with the true parameter values that simply means optimality regardless of the problematical outliers in the observations. (2) We propose how robust uncertainty computations and robust model selection can be performed in the similar, consistent manner. (3) We derive the expectation maximisation algorithm for the estimator and (4) extend the estimator to handle unknown outlier residual distributions. (5) We finally give some experiments with real data, where robustness in model fitting is needed. Sami Brandt received the degree of Master of Science in Technology from the department of Engineering Physics and Mathematics in Helsinki University of Technology, Finland, in September 1999 and the degree of Doctor of Science in Technology at the Laboratory of Computational Engineering, Helsinki University of Technology, in October 2002. After serving one year as a research scientist in Instrumentarium Corporation Imaging Division and two years as a post-doc at LCE, he is currently jointly affiliated at LCE and Information Processing Laboratory, University of Oulu, Finland; and he focuses research on bio-medical imaging and 3D vision. He is a member of the IEEE and IEEE Computer Society, member of the Pattern Recognition Society of Finland, member of the International Association for Pattern Recognition (IAPR), and member of the Finnish Inverse Problems Society.     

极大似然估计的递推计算灵敏度算法

极大似然估计的递推计算灵敏度算法崔平远,吴瑶华,黄文虎,李乃宏（哈尔滨工业大学航天工程与力学系１５０００１）关键词连续－离散系统,极大似然估计,灵敏度,正交试验玉引言采用极大似然法对连续一离散系统进行参数估计,实际上是一个似然函数的优化计算问题,而其...     

最大似然估计ROC凸包曲线算法

采用列联表表示特定阈值下入侵检测系统(IDS)的性能,使用ROC曲线对不同阈值下IDS的总体性能进行评估。在实际应用中,基于样本数据集,通过计算几何的方式得到ROC凸包曲线(ROCCH)。为降低ROCCH的计算复杂度,在曲线下面积(AUC)最大似然估计的条件下,通过保序回归得到最大似然估计ROC曲线(MLE-ROC)算法。实验表明,MLE-ROC算法在降低计算复杂度的同时提高了AUC的近似程度。     

Maximum Likelihood Multi-innovation Stochastic Gradient Estimation for Multivariate Equation-error Systems

International Journal of Control, Automation and Systems - This paper focuses on the parameter estimation problems of multivariate equation-error systems. A multi-innovation generalized extended...     

基于实数遗传算法的波达方向最大似然估计算法

对空间多个窄带信号源的高分辨波达方向估计是雷达,块纳和地震等信号处理中的重要问题之一,为克服一些获取波达方向估计最大似然解算法存在的局部极值问题,提高估计精度,本文以作者提出的实数遗传算法为搜索工具,寻求波达方向最大似然估计的非线性全局最优解,所提出的实数遗传算法由含实数域结构和目标函数信息的实数交叉和变异算子构成,是较理想的获取非线性实变量函数全局最优解的方法,对非相参和全相参信源波达方向估计问题的数字仿真结果表明,本方法的估计精度明显优于交替极值等一些常规方法。     

递推计算灵敏度的极大似然估计算法实现

对非线性连续一离散系统的极大似然参数估计方法导出了一种递推计算灵敏度的新算式.该算式借助二水平正交表的性质,避免了原灵敏度递推算式中的矩阵求逆运算.仿真实例验证了该算法的实用性和有效性.     

雷达高度计中最大似然算法的应用

首先从分析国外星载雷达高度计中的自适应处理器入手，介绍了最大似然算法的基本理论，旨在分析最大似然算法用于雷达高度计的技术方法，并对有关公式进行了详细的推导，并对国外有关文献中的一些公式的推导错误进行了纠正。     

改进的非线性连续-离散系统的极大似然参数估计及其应用

对Jategaonkar等人给出的同时计及过程及观测噪声的非线性连续-离散系统的极大似然算法从两个方面进行了改进:1)给出了计算灵敏度的最佳摄动有限差分算法,避免了普通有限差分法计算灵敏度矩阵时需人为选择参数摄动量大小而带来的缺点;2)给出了具有快速三角化平方根滤波的极大似然算法,提高了原算法的数值稳定性.上述改进算法经应用于飞行器系统参数估计证明了方法的有效性.     

The Fixed-Point Algorithm and Maximum Likelihood Estimation for Independent Component Analysis

The author previously introduced a fast fixed-point algorithm for independent component analysis. The algorithm was derived from objective functions motivated by projection pursuit. In this paper, it is shown that the algorithm is closely connected to maximum likelihood estimation as well. The basic fixed-point algorithm maximizes the likelihood under the constraint of decorrelation, if the score function is used as the nonlinearity. Modifications of the algorithm maximize the likelihood without constraints.     

基于最大似然估计的加权质心定位算法

为解决无线传感器网络中节点自身定位问题,针对接收信号强度指示（received signal strength indication,RSSI）测距误差大和质心定位算法精度低的问题,提出一种基于最大似然估计的加权质心定位算法．首先通过计算将估计距离与实际距离之间的最大似然估计值作为权值,然后在权值模型中,引进一个参数k优化未知节点周围锚节点分布,最后计算出未知节点的位置并加以修正．仿真结果表明,基于最大似然估计的加权质心算法具有定位精度高和成本低的特点,优于基于距离倒数的质心加权和基于RSSI倒数的质心加权算法,适用于大面积的室内定位．     

基于最大似然估计的不可区分多目标定位法

传统的最大似然估计定位法无法剔除误差较大或错误的参数,且不适用于多目标情形。结合最大似然估计理论,针对不可区分多目标定位问题,引出了目标位置信息场定位法,通过加入代价函数使得该方法能够剔除错误数据,降低误差大的数据对定位结果估计的影响。仿真结果表明,目标位置信息场定位法具有筛选有用参数信息的能力,且适用于不可区分的多目标定位,能够同时确定目标数目和位置,验证了方法的有效性。