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/359187/6841936
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/359187/6841957
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/473487/6844239
m awgn.m
%*************************************************************************************
% This function pertains to the addition of AWGN with mean zero and
% parameter 'variance' to
www.eeworm.com/read/294645/8213431
m awgn.m
%*************************************************************************************
% This function pertains to the addition of AWGN with mean zero and
% parameter 'variance' to
www.eeworm.com/read/392443/8342047
m confint.m
function [k2,k1] = confint (g,m,S2)
% [k2,k1] = confint (g,m,S2)
%
% Confidence intervals for the structure function
%
% CONF {k2
www.eeworm.com/read/392443/8342056
m fitvario.m
function fitvario (model,data,a,b)
% fitvario (model,data,a,b)
%
% Fonction qui permet d'obtenir la combinaison optimale des
% param鑤res 'a', 'b' et 'c' de la fonction 'variogr.m'
%
% Input:
www.eeworm.com/read/173705/9641001
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/173453/9657330
m mod2trans.m
No=variance;
tx_waveform=bpsk(u,1); %amp= 1
rx_waveform=awgn(tx_waveform,No);
www.eeworm.com/read/171050/9774278
m awgn.m
%*************************************************************************************
% This function pertains to the addition of AWGN with mean zero and
% parameter 'variance' to
www.eeworm.com/read/415311/11077102
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