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/399996/7816798
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/299920/7820743
txt inputparameters.txt
5000
150
30
151
*************************************************
Note: first line -- number of symbols sent *
second line -- starting variance *
third line -- step f
www.eeworm.com/read/299920/7820751
cpp channel.cpp
// definition of channel
#include "GaussianNoiseGenerator.cpp"
class channel
{
private:
long seed;
double variance;
public:
channel(){ seed = -300; variance=1;} /* seed must be
www.eeworm.com/read/299869/7827607
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/399528/7850451
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/298491/7958608
m awgn.m
%*************************************************************************************
% This function pertains to the addition of AWGN with mean zero and
% parameter 'variance' to
www.eeworm.com/read/398034/8009032
m sa_ex8_5.m
%Minimum variance Array Weights
% example 8.5
d=.5;
N= 5;
sig2=.001; % noise variance
theta=-pi/2:.01:pi/2;
ang=theta*180/pi;
th0=30*pi/180; % receive angle
th1=-10*pi/180;
s=1;
www.eeworm.com/read/197100/8029728
cpp clusttool.cpp
/******************************************************************************
** Filename: clustertool.c
** Purpose: Misc. tools for use with the clustering routines
** Author: Dan Johnson
** H
www.eeworm.com/read/397099/8068828
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/397099/8068841
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