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/368337/9701172
out stat.out
The dataset is 17.2, 18.1, 16.5, 18.3, 12.6
The sample mean is 16.54
The estimated variance is 4.2984
The largest value is 18.3
The smallest value is 12.6
www.eeworm.com/read/170937/9779029
m y2res.m
function [R]=y2res(Y)
% Y2RES evaluates basic statistics of a data series (column)
% res=y2res(y)
%
% OUTPUT:
% res.N sum (number of samples)
% res.MU mean
% res.SD2 variance
%
www.eeworm.com/read/270032/11051076
c wave.c
/* ------------------------------------------------------------------------- *\
WAVE.C :
This package provides 3 routines for generating rectangular-like,
triangular-like and sine-like waves
www.eeworm.com/read/335820/12496693
m kf_opt.m
% KF_opt - using discrete Kalman filter for obtaining
% multiple parameters estimations dependence on time based upon an observation.
%
% INTRODUCTION
% The problem of applying Kalman filtering f
www.eeworm.com/read/334951/12558635
cpp xtutest.cpp
#include
#include
#include
#include "nr.h"
using namespace std;
// Driver for routine tutest
int main(void)
{
const int NPTS=5000,MPTS=1000,NSHFT=10;
www.eeworm.com/read/248284/12585612
m noisepwr1.m
function nvar = noisepwr1(num,den)
% Computes the output noise variance due
% to input quantization of a digital filter
% based on a partial-fraction approach
%
% num and den are the numerator an
www.eeworm.com/read/237896/13920978
m fig9_28.m
clear all
npts = 2000;
del = 1/2000;
t = 0:del:1;
inp = (1+.2 .* t + .1 .*t.^2) + cos(2. * pi * 2.5 .* t);
X0 = [1,.1,.01]';
% it is assumed that the measurement vector H=[1,0,0]
% this is the
www.eeworm.com/read/237896/13920980
m fig9_27.m
clear all
npts = 2000;
del = 1/2000;
t = 0:del:1;
inp = (1+.2 .* t + .1 .*t.^2);% + cos(2. * pi * 2.5 .* t);
X0 = [1,.1,.01]';
% it is assumed that the measurmeny vector H=[1,0,0]
% this is the
www.eeworm.com/read/236491/14013910
m bootstrap.m
function [x,q] = bootstrap(actualx,y,R,Q,initVar,numSamples);
% PURPOSE : This m file performs the bootstrap algorithm (a.k.a. SIR,
% particle filter, etc.) for the model specified in the
%