代码搜索：Variance
找到约 2,271 项符合「Variance」的源代码
代码结果 2,271
www.eeworm.com/read/420934/10766983
asv main.asv
clear all
close all
%------ construction d'une elevation synthtetique = une Gaussienne-------
% x=[-5:0.0469:5] ;
% y=x ;
x=1:256;
y=x;
pixelx=length(x) ;
pixely=len
www.eeworm.com/read/420934/10766998
m main.m
clear all
close all
%------ construction d'une elevation synthtetique = une Gaussienne-------
x=[-5:0.0469:5] ;
y=x ;
% x=1:256;
% y=x;
pixelx=length(x) ;
pixely=len
www.eeworm.com/read/420934/10767015
m main1.m
clear all
close all
%------ construction d'une elevation synthtetique = une Gaussienne-------
x=1:128 ;
y=x ;
pixelx=length(x) ;
pixely=length(y) ;
[X,Y]=mes
www.eeworm.com/read/420934/10767019
m heigth2d.m
%Construct test surface
x=[-4:0.08:4];
y=[-4:0.08:4];
pixelx=length(x) ;
pixely=length(y) ;
hinitial=zeros(length(x),length(y));
for i=1:length(x)
for j=1:length(y)
www.eeworm.com/read/420614/10786530
m awgn.m
%**************************************************************************
%Function:AWGN channel
%功能：产生AWGN信道
%function y=awgn(x,var)
%Input: x ---> input signal
%Input: var ---> variance
%
www.eeworm.com/read/349842/10796785
m mean_jackknife.m
function [mu, bias, varjack] = mean_jackknife(data)
%Find the estimate of the mean, it's bias and variance using the jackknife estimator method
%Inputs:
% data - The data from which to estimate
www.eeworm.com/read/349842/10796797
m mean_bootstrap.m
function [mu, bias, varjack] = mean_bootstrap(data, B)
%Find the estimate of the mean, it's bias and variance using the bootstrap estimator method
%Inputs:
% data - The data from which to estimat
www.eeworm.com/read/348694/10874364
m contents.m
% Bootstrap Toolbox
%
% Communications & Information Processing Group
% Cooperative Research Centre for Satellite Systems
% School of Electrical & Electronic Systems E
www.eeworm.com/read/418157/10963714
m iniadc_dac.m
function [Vthreshold,ncap,DAClevelIDEAL,DAClevelREAL] = iniADC_DAC(NF,k,MM,LF,CST,VV,match,VVG)
% Determines the ADC thresholds and the DAC levels, the input and output range
% is between -1 and +1
www.eeworm.com/read/469123/6977830
m covnoise.m
function [A, B] = covNoise(logtheta, x, z);
% Independent covariance function, ie "white noise", with specified variance.
% The covariance function is specified as:
%
% k(x^p,x^q) = s2 * \delta(p,q)