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5-27-CLT
Central Limit Theorem
CLT
Example
Normal Approximation to the Binomial
- 2010-07-19 13:23:30下载
- 积分:1
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fit_ML_log_normal
fit_ML_normal - Maximum Likelihood fit of the laplace distribution of i.i.d. samples!.
Given the samples of a laplace distribution, the PDF parameter is found
fits data to the probability of the form:
p(x) = 1/(2*b)*exp(-abs(x-u)/b)
with parameters: u,b
format: result = fit_ML_laplace( x,hAx )
input: x - vector, samples with laplace distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
u,b - fitted parameters
CRB_b - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type - ML
- 2011-02-09 19:07:30下载
- 积分:1
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kmeanforvoice
说明: K-MEAN:经典K均值算法,适用领域:语音识别,图像识别(K-MEAN: classical K-means algorithm, the application areas: speech recognition, image recognition)
- 2008-09-11 20:18:05下载
- 积分:1
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quxiannihe
拟合函数为 g(x)=0.52253+7.90342*x-7.129385*sin(x)(Fitting function for g (x) = 0.52253+7.90342* x-7.129385* sin (x))
- 2009-03-17 10:29:28下载
- 积分:1
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Solving-Nonlinear-Equations-using-Newton-Raphson
Nonlinear equation solver
- 2014-09-08 02:27:11下载
- 积分:1
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Earthquake-prediction
可以用来进行地震预测的matlab代码,直接模仿就可以用了(Can be used for earthquake prediction matlab code, you can use a direct imitation)
- 2013-10-01 22:12:38下载
- 积分:1
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DeepCCA-master
深度典型相关分析的python实现,DCCA是Galen Andrew于2013年发表于ICML上的一篇文章,提出了将深度网络与CCA结合的新方法。(Deep canonical correlation analysis)
- 2018-09-20 12:43:05下载
- 积分:1
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speechdtwprotected
dynamic time warp Matlab speech recognition source code DTW (dynamic time consolidation), the Speech Recognition Research counterparts can be downloaded
- 2009-05-07 03:41:34下载
- 积分:1
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qpsk_16psk_pam
仿真了QPSK,16PSK与QAM信号在不同通信信道中的误码率。(simulation of QPSK, 16QAM and QAM signals in different communication channel bit error rate.)
- 2007-05-06 21:12:13下载
- 积分:1
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Reactive-power-compensation-MATLAB
过零无功功率补偿的研究及其matlab仿真(Zero-crossing compensation)
- 2013-05-04 17:33:57下载
- 积分:1