代码搜索：ESTIMATION
找到约 3,786 项符合「ESTIMATION」的源代码
代码结果 3,786
www.eeworm.com/read/361768/10036639
m fattailed_garchlikelihood.m
function [LLF, h, likelihoods] = fattailed_garchlikelihood(parameters , data , p , q, errortype, stdEstimate, T)
% PURPOSE:
% Likelihood for fattailed garch estimation
%
% USAGE:
% [LLF,
www.eeworm.com/read/422073/10666020
m stbcxindaoguji.m
%for ML channel estimation
frmLen = 100; % frame length
maxNumErrs = 300; % maximum number of errors
maxNumPackets = 3000; % maximum number of packets
EbNo = 0:2:12; %
www.eeworm.com/read/418695/10935439
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/466694/7031461
gka-readme
GKA
Gaussian Kernel Algorithm Correlation Dimension Estimation
Estimatation of correlation dimension, entropy and noise level using the
GKA algorithm.
INSTALLATION
- Place the contents of this arch
www.eeworm.com/read/299984/7140317
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)
%
www.eeworm.com/read/460435/7250792
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)
%
www.eeworm.com/read/449504/7502048
m becm.m
function results = becm(y,nlag,tight,weight,decay,r)
% PURPOSE: performs Bayesian error correction model estimation
% using Minnesota-type prior
%---------------------------------------------
www.eeworm.com/read/449504/7502065
m ecm.m
function results = ecm(y,nlag,r)
% PURPOSE: performs error correction model estimation
%---------------------------------------------------
% USAGE: result = ecm(y,nlag,r)
% where: y = an (nobs
www.eeworm.com/read/449504/7502199
m olsrs.m
function results = olsrs(y,x,R,q)
% PURPOSE: Restricted least-squares estimation
% y = Xb + e with the constraint that q = Rb
%---------------------------------------------------
% USAGE: results
www.eeworm.com/read/449504/7502759
m fattailed_garchlikelihood.m
function [LLF, h, likelihoods] = fattailed_garchlikelihood(parameters , data , p , q, errortype, stdEstimate, T)
% PURPOSE:
% Likelihood for fattailed garch estimation
%
% USAGE:
% [LLF,