代码搜索：ESTIMATION
找到约 3,786 项符合「ESTIMATION」的源代码
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www.eeworm.com/read/422590/10627314
rd cde.bandwidths.rd
\name{cde.bandwidths}
\alias{cde.bandwidths}
\title{Bandwidth calculation for conditional density estimation}
\description{
Calculates bandwidths for kernel conditional density estimates. Methods
www.eeworm.com/read/420575/10788641
m minque.m
function d0=minque(B,L,T,d0)
% variance-ccovariance estimation based on MINQUE
% E(L)=B*X
% e=d1*T1+d2*T2+...+d3*T3;
b=size(T);
m=b(length(b));%subtract the number of variance components
TT=zer
www.eeworm.com/read/420575/10788645
asv minque.asv
function d0=minque(B,L,T,d0)
%variance-ccovariance estimation based on MINQUE
%E(L)=B*X
% e=d1*T1+d2*T2+...+d3*T3
b=size(T);
m=b(length(b));%subtract the number of variance components
TT=zeros(
www.eeworm.com/read/420575/10788648
m minqe.m
function d0=minqe(B,L,T,d0)
%variance-ccovariance estimation based on MINQUE
b=size(T);
m=b(length(b));%subtract the number of variance components
TT=zeros(length(L));
d=d0;
while 1
%for t=1:2
www.eeworm.com/read/416907/11010036
txt description.txt
Summary: This simulation simulates coded OFDM using RS Code over wireless channel.
MATLAB Release: R13
Required Products: Simulink
Description: This simulation simulate wireless Coded OFD
www.eeworm.com/read/299984/7140008
m testk.m
%TESTK Error estimation of the K-NN rule
%
% E = TESTK(A,K,T)
%
% INPUT
% A Training dataset
% K Number of nearest neighbors (default 1)
% T Test dataset (default [], i.e. find leave-one-out e
www.eeworm.com/read/314856/7146581
m armaorder.m
function order=orderest(mo,sig2,N,nu);
% function orderest(mo,sig2,N,nu);
%
% Order estimation for a generic ARMA model
%
% inputs:
% mo: vector of model orders
% sig2: vector mean square er
www.eeworm.com/read/462846/7194325
txt readme.txt
List of Matlab Files in this ZIP file
---------------------------------------
ProbX_Y.m: used to solve problem X.Y
Motion_Estimation_2D.m: estimate block wise motion vectors between two video
www.eeworm.com/read/460435/7250483
m testk.m
%TESTK Error estimation of the K-NN rule
%
% E = TESTK(A,K,T)
%
% INPUT
% A Training dataset
% K Number of nearest neighbors (default 1)
% T Test dataset (default [], i.e. find leave-one-out e
www.eeworm.com/read/450608/7480125
m testk.m
%TESTK Error estimation of the K-NN rule
%
% E = TESTK(A,K,T)
%
% INPUT
% A Training dataset
% K Number of nearest neighbors (default 1)
% T Test dataset (default [], i.e. find leave-one-out e