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/388600/2548987
tex paper.tex
% Started 11/29/00
%\shortnote
\lefthead{}
\righthead{}
\title{Multidimensional recursive filter preconditioning \\
in geophysical estimation problems}
%\renewcommand{\author}[1]{%
%\begin{cent
www.eeworm.com/read/388600/2549017
bib spitz.bib
@Book{gee,
author = {J[on] Claerbout},
title = {{G}eophysical estimation by example: {E}nvironmental soundings image enhancement},
publisher = {Stanford Exploration Project},
y
www.eeworm.com/read/394734/8210346
m meancov.m
%MEANCOV Estimation of the means and covariances from multiclass data
%
% [U,G] = MEANCOV(A,N)
%
% INPUT
% A Dataset
% N Normalization to use for calculating covariances: by M, the number
%
www.eeworm.com/read/414142/11126390
m est.m
function [est_coefs,signal]=est(signal,ch_coefs,frames,sim_options)
%**************************************************************************
%This file carriers out channel estimation based on
www.eeworm.com/read/147096/12584950
m cubic.m
function [maximum,err]=cubic(pts,checkpt,location)
%CUBIC Cubicly interpolates four points to find the maximum value.
% The second argument is for estimation of the error in the
% in
www.eeworm.com/read/203108/15365424
m est.m
function [est_coefs,signal]=est(signal,ch_coefs,frames,sim_options)
%**************************************************************************
%This file carriers out channel estimation based on
www.eeworm.com/read/101557/15826889
m cubic.m
function [maximum,err]=cubic(pts,checkpt,location)
%CUBIC Cubicly interpolates four points to find the maximum value.
% The second argument is for estimation of the error in the
% in
www.eeworm.com/read/289027/8584367
m djiaestimate.m
function [DateHistory, RetHistory, PortHistory, X, Y, Z ] ...
= DJIAestimate(Asset, Date, Data, Map, Reference)
%DJIAestimate BlueChipStock estimation and analysis for DJIA period
Window = 60;
www.eeworm.com/read/431675/8662095
m meancov.m
%MEANCOV Means and covariance estimation from multiclass data
%
% [U,G] = meancov(A)
%
% Computation of a set of mean vectors U and a set of covariance
% matrices G of the classes in the dataset A
www.eeworm.com/read/386050/8768120
m parzenml.m
%PARZENML Optimum smoothing parameter in Parzen density estimation.
%
% H = PARZENML(A)
%
% INPUT
% A Input dataset
%
% OUTPUT
% H Scalar smoothing parameter (in case of crisp labels)
%