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线性离散系统的批处理最小二乘参数估计的例程，噪声为方差为1的白噪声-Batch least squares linear discrete systems parameter estimation routine, the noise is white noise variance of 1
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赫尔模特方差分量估计C#实现
赫尔模特方差分量估计
赫尔模特方差分量估计
-Hull model estimates variance components model C# implementation of Hull Hull variance component estimation of variance components estimation model
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mucic和espric算法在估计偏差，成功概率，估计方差图形中的比较。-mucic and espric bias in the estimation algorithm, the probability of success, estimated variance graph comparison.
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当输入由白噪声产生的随机信号时，使期望输出和实际输出之间的均方根误差达到最小的线性系统。一种以状态变量的线性最小方差递推估算的方法。-When the input generated by the random white noise signal, the desired output and actual output of the RMS error to a minimum between the linear system. A kind of state variables of t
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music算法的性能分析 包括成功概率 估计方差 估计误差 的源代码-music algorithm performance analysis, including probability of success is estimated that the source code of the estimation error variance
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测绘领域，使用C#语言编写的方差分量估计定权程序源代码。-Mapping the field, written in C# given the right to variance component estimation program source code.
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DOA估计的算法，其中包括Music算法、空间平滑Music算法、Root-Music算法、ESPRIT算法MVDR（最小方差无失真）算法等，便于比较-DOA estimation algorithms, including algorithms Music, Music spatial smoothing algorithm, Root-Music algorithm, ESPRIT algorithm MVDR (minimum variance distortion) algorithm,
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对于信号 ，其中w(n)为均值为0，方差为1的AWGN。n=1,2,…，128。
AR模型功率谱估计。假设AR（4），用LS方法估计AR参数，功率谱用freqz（1，LS_ar,1024,1）来验证。-For the signal, which w (n) is zero mean and variance 1 AWGN. n = 1,2, ..., 128.
AR model power spectrum estimation. Assuming AR (4), with the
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这是模式识别中最小错误率Bayes分类器设计方案。
自行完善了在不同先验概率的条件下，男、女错误率和总错误率的统计，放入各个数组当中。
全部程序由主函数、最大似然估计求取概率密度子函数、最小错误率贝叶斯分类器决策子函数三块组成。
调用最大似然估计求取概率密度子函数时，第一步获取样本数据，存储为矩阵；第二步对矩阵的每一行求和，并除以样本总数N，得到平均值向量；第三步是应用公式（3-43）采用矩阵运算和循环控制语句，求得协方差矩阵；第四步通过协方差矩阵求得方差和相关系数，从而得到概率密度
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这是模式识别中最小风险Bayes分类器的设计方案。在参考例程的情况下，自行完善了在一定先验概率的条件下，男、女错误率和总错误率的统计，放入各个数组当中。
全部程序由主函数、最大似然估计求取概率密度子函数、最小错误率贝叶斯分类器决策子函数三块组成。
调用最大似然估计求取概率密度子函数时，第一步获取样本数据，存储为矩阵；第二步对矩阵的每一行求和，并除以样本总数N，得到平均值向量；第三步是应用公式（3-43）采用矩阵运算和循环控制语句，求得协方差矩阵；第四步通过协方差矩阵求得方差和相关系数，从
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I wrote these functions for GARCH(1,1) estimation with variance targeting. They should be used TOGETHER with the MFEToolbox available at http://www.kevinsheppard.com/wiki/MFE_Toolbox.
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基于EKF的神经网络自适应在线学习算法，包含例子和文档。-We show that a hierarchical Bayesian modeling approach allows us to perform
regularization in sequential learning. We identify three inference
levels within this hierarchy: model selection, parameter estimation, and
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function Px = music(x,p,M)
MUSIC Frequency estimation using the MUSIC algorithm.
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USAGE Px=music(x,p,M)
The input sequence x is assumed to consist of p complex
exponentials in white noise. The frequencies of the
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DOA估计：最大熵算法、最小模算法、最小方差算法。输入： 快拍数据协方差矩阵R，观察范围内各方向导向矢量，三维矩阵phiyz。输出：最小方差谱估计矩阵P。-DOA Estimation: maximum entropy algorithm, the minimum norm algorithm, the minimum variance algorithm. Input: snapshot of the data covariance matrix R, the steering vector
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最小方差响应不变波束形成器即MVDR是一种高分辨谱估计方法，MVDR在信号处理DOA中应用较广。-Minimum variance beamformer response constant MVDR is a kind of high resolution spectrum estimation method in signal processing, MVDR DOA has a wide application
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白噪声中单个正弦信号的频率检测与估计
产生，其中是一高斯白噪声，其均值为0，方差为1。用MUSIC方法估计观测数据中正弦波的频率，并给出白噪声方差()un2uσ 与复正弦波的振幅A的估计值。-A single frequency detection and estimation of the sinusoidal signal in white noise, which is a Gaussian white noise with mean 0 and variance 1. MUSIC m
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三个干扰数目下信噪比同成功概率、估计方差等的关系 -The interference of three number of signal to noise ratio with probability of success, such as the relationship between the variance of estimation
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本文介绍MVDR算法实现及其改进 用于多麦克风语音增强-The minimum variance distortionless response (MVDR),
originally developed by Capon for frequency-wavenumber
analysis, is a very well established method in array process-ing. It is also used in spectral estimation. The
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The method of maximum likelihood corresponds to many well-known estimation methods in statistics. For example, one may be interested in the heights of adult female giraffes, but be unable to measure the height of every single giraffe in a population
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根据方差最小的原则通过对系数的无偏似然估计确定阈值-According to the principle of minimum variance coefficient no partial likelihood estimation to determine the threshold
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