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k_means
- k-means 算法接受输入量 k ；然后将n个数据对象划分为 k个聚类以便使得所获得的聚类满足：同一聚类中的对象相似度较高；而不同聚类中的对象相似度较小。聚类相似度是利用各聚类中对象的均值所获得一个“中心对象”（引力中心）来进行计算的。-In statistics and machine learning, k-means clustering is a method of cluster analysis which aims to partition n observations into
expectationMaximization
- expectation Maximization
GMMEMDEMO
- This matlab code implements the expectation-Maximization algorithm to estimate the parameters of a gaussian mixture model.
Correlator
- a program is generating a signal (harmonic or chirp signal (linear-frequency modulation)) for performing correlation function, which was noised by gaussian or with a noise has a uniform distribution with appropriate parameters (expectation value and
bpm
- Thomas Minka 编写的适用于Bayesian Point Machine，expectation Propagation计算的matlab工具箱。对科研人员很有价值。 -This toolbox implements the EP algorithms described in Thomas Minka s thesis and UAI paper.
KMean
- KMEAN C# In data mining, k-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. This results in a partitioning of the data sp
multiScale_KalmanFilter
- 用多尺度卡尔曼滤波法，对信号参数进行识别估计。高频信号和低频信号识别结合起来改进了算法识别的精确度和准确度。-It is an implementation of hierarchical (a.k.a. multi-scale) Kalman filter using belief propagation. The model parameters are estimated by expectation maximization (EM) algorithm. In this impleme
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
random
- function Rguji=shixi2_2(t,thetaa1,thetaa2,thetab1,thetab2,thetac,mm) t 是要求系统生存的寿命 thetaa1 是元件A1的数学期望 thetaa2 是元件A2的数学期望 thetab1 是元件B1的数学期望 thetab2 是元件B2的数学期望 thetac是元件c的数学期望-
Equations
- expectation maximization example program we know as EM algorithm.
EM
- 自己编写的期望最大化（EM）算法的MATLAB实现，里面有较为运行方法和程序说明，对新手有很好的帮助- I have written the expectation-maximization (EM) algorithm in MATLAB, which has run more methods and procedures described, there is a good help for the novice
CHW3
- expectation Maximization (EM) Algorithm for Gaussian Mixture Model
em
- 在统计计算中，最大期望（EM）算法是在概率模型中寻找参数最大似然估计或者最大后验估计的算法，其中概率模型依赖于无法观测的隐性变量。最大期望算法经常用在机器学习和计算机视觉的数据聚类（Data Clustering）领域。(In statistical computation, the maximum expectation (EM) algorithm is an algorithm to find the maximum likelihood estimation or the maximum