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/396834/8087902
m pca_an.m
% eigenvector projection (arbitrary dimension)
% argument is pattern matrix (data) and desired output dimensionality (odim)
% noutf - number of output attributes
% Output: rpm - returns projecte
www.eeworm.com/read/145742/12704648
m awgn.m
%*************************************************************************************
% This function pertains to the addition of AWGN with mean zero and
% parameter 'variance' to
www.eeworm.com/read/145525/12717054
c mrandom.c
#include
#include
#include
#include "msp.h"
float randnu(long *iseed)
{
float z;
*iseed=2045*(*iseed)+1;
*iseed=*iseed-(*iseed/1048576)*10
www.eeworm.com/read/332494/12752429
m af_cnmpaad.m
function sig2=af_usrcnmpaad(del_t,el)
%*************************************************************************
%* Copyright c 2001 The board of trustees of the Leland Stanford *
%*
www.eeworm.com/read/245941/12770891
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/245941/12770908
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/245863/12776291
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/330850/12864897
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/330850/12864921
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