搜索资源列表
CML
- 条件极大似然算法(Conditional Maximum Likelihood)的实现:使用十字型天线阵列对信号源的来波方向(仅限于方位角)进行估计(DOA估计),可以选择信号的传播信道为高斯,瑞利或是莱斯信道。-A program achieves DOA Estimation using Conditional Maximum Likelihood Method,and its array it s a across one.
Time-series-analysis
- 现代信号处理中时间序列分析,相关重要算法,包括逆函数预测算法,矩估计,极大似然估计, 格林函数预测算法,差分方程预测方法-Modern signal processing, time series analysis, the related important algorithms, including inverse function prediction algorithms moment estimation, maximum likelihood estimation, predic
ML-estimator
- 极大似然估计器用于简单通信系统模拟,估计A1,A2: s1 = x11*A1 + x12*A2 s2 = x21*A1 = x22*A2 r1 = s1 + n1 r2 = s2 + n2-Maximum likelihood estimator for a simple communication system simulation, it is estimated that A1, A2: s1 = x11* A1+ x12* A2 s2 = x21* A1 = x22
RML
- 极大似然估计法用于系统模型辨识,需要构造一个以观测数据和未知参数为自变量的似然函数。-Maximum likelihood estimation method for system model identification, the need to construct a likelihood function as independent variables to the observed data and the unknown parameters.
EM-suanfa-hunhegaosi
- em算法计算混合高斯模型的参数估计,极大似然,EM算法用于K均值问题的参数估计。MATLAB实现有代码-em algorithm Gaussian mixture model parameter estimation, maximum likelihood parameter estimation for K-means problem EM algorithm. MATLAB implementation code
liu
- 状态模型的极大似然估计,使用EM算法,以及卡尔曼滤波。-This supplementary note discusses the maximum likelihood esti-mation of state space models using Expectation-Maximization (EM) algorithm and bootstrap procedure for statistical inference. A Matlab program scr ipt impleme
RML
- 递推极大似然用于系统辨识的MATLAB实例-Recursive maximum likelihood for system identification examples of MATLAB
mle
- 给定数据和相应的概率密度函数、用matlab求解其相应的极大似然估计-Given data and the corresponding probability density function using matlab to solve the corresponding maximum likelihood estimation
my_mle
- 极大似然估计的matlab算法,从其他网站得来的,希望对大家有用-Maximum likelihood estimation matlab algorithms come from other sites, we hope to be useful
ppnew
- 极大似然估计是一种很有用的办法,可以用MATLAB来实现,事实上,极大似然与最小二乘估计结果是相同的-MLE is a very useful way, you can use MATLAB to achieve, in fact, maximum likelihood and least squares estimation results are the same
ML.m
- 在贝叶斯分类中,用极大似然估计法估计概率分布的均值和方差-Compute the maximum-likelihood estimate of the mean and covariance matrix of each class and then uses the results to construct the Bayes decision region. This classifier works well if the classes are uni-modal, even when
System-Identification
- 1、经典粒子群算法 2、一次最小二乘法(LS) 3、递推最小二乘算法(RLS) 4、确定性系统的递推梯度校正参数估计(RGC) 5、递推极大似然参数估计算法(RML)-1、Classical particle swarm optimization algorithm matlab code 2、Various types of system identification algorithm
kafangcanshuguji
- 总体X服从非中心卡方分布,求两个参数的极大似然估计 输入:样本数据 输出:参数估计值n,v 内有详细说明-Overall X obey non-central chi-square distribution, find the two parameters MLE
matlab
- test2: 一、 基本最小二乘法一次算法 二、 基本最小二乘法递推算法 三、 最小二乘遗忘因子一次完成算法 四、 最小二乘遗忘因子递推算法 五、 最小二乘限定记忆算法 六、 最小二乘偏差补偿算法 七、 增广最小二乘算法 八、 广义最小二乘算法 test3: 一、 辅助变量自适应滤波算法 二、 辅助变量纯滞后算法 三、 辅助变量Tally原理算法 四、 多级最小二乘算法 五、 各类改进最小二乘算法的特点 test4: 1、 第二类随
extreme_fit
- 极值分布的参数的极大似然估计,并绘图比较-Extreme value distribution parameters of maximum likelihood estimation, and drawing comparison
the-maximum-likelihood-estimate
- 1、 极大似然估计 尝试用0~24阶多项式拟合,并用5折交叉验证选择最佳模型(多项式阶数及其系数,给出类似课件中的图),并画出最佳模型的拟合效果图(类似图1,蓝色点为训练样本、红色点为测试样本、绿色线为模型预测),给出该模型的测试误差。 2、 岭回归 多项式阶数为24,正则系数λ的取值范围为exp(-19)到exp(20),采用并用5折交叉验证选择最佳模型。实验结果要求同1。 -1, the maximum likelihood estimate of 0 to 24 try-o
WSN_LOCALIZATION_SIMULATION
- 用MATLAB语言仿真无线传感器网络中三边定位法、极大似然值定位法、质心定位法和泰勒级数展开定位法-Using MATLAB simulation of wireless sensor networks trilateral positioning method, maximum likelihood value positioning method, centroid location positioning method and Taylor series expansion method
ridge_regression_matlab
- 岭回归拟合数据,其中hw3_1_ridge.m使用的是岭回归的方法,hw3_1_MLE.m使用的是极大似然的方法。-fitting data with ridge regression using matlab
tremendorsimilar
- 对图片中的颗粒对象进行统计,统计周长,面积,等效直径等特征,利用数值计算实现极大似然估计估计统计结果的weibull双参数。-Pictures of the objects particle statistics, statistics perimeter, area, equivalent diameter and other characteristics, is estimated using the maximum likelihood estimation of statistical
hmmtrain
- 隐马尔可夫模型参数的极大似然估计,用来求解HMM的第三个问题-HMM maximum likelihood parameter estimates used to solve the third problem HMM