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/349842/10796827
m bayesian_parameter_est.m
function [mu, sigma] = Bayesian_parameter_est(train_features, train_targets, sigma, region)
% Estimate the mean using the Bayesian parameter estimation for Gaussian mixture algorithm
% Inputs:
%
www.eeworm.com/read/468911/6981765
txt readme.txt
vebyk (Value Estimation BY Kriging)
vebyk is a flexible and user-friendly matlab-program, which performs ordinary kriging and can be easily adapted to other kriging methods.
The program is desig
www.eeworm.com/read/299984/7140055
m normal_map.m
%NORMAL_MAP Map a dataset on normal-density classifiers or mappings
%
% F = NORMAL_MAP(A,W)
%
% INPUT
% A Dataset
% W Mapping
%
% OUTPUT
% F Density estimation for classes in A
%
% DESC
www.eeworm.com/read/299984/7140320
m crossval.m
%CROSSVAL Error/performance estimation by cross validation (rotation)
%
% [ERR,CERR,NLAB_OUT] = CROSSVAL(A,CLASSF,N,1,TESTFUN)
% [ERR,STDS] = CROSSVAL(A,CLASSF,N,NREP,TESTFUN)
% R
www.eeworm.com/read/460435/7250530
m normal_map.m
%NORMAL_MAP Map a dataset on normal-density classifiers or mappings
%
% F = NORMAL_MAP(A,W)
%
% INPUT
% A Dataset
% W Mapping
%
% OUTPUT
% F Density estimation for classes in A
%
% DESC
www.eeworm.com/read/460435/7250795
m crossval.m
%CROSSVAL Error/performance estimation by cross validation (rotation)
%
% [ERR,CERR,NLAB_OUT] = CROSSVAL(A,CLASSF,N,1,TESTFUN)
% [ERR,STDS] = CROSSVAL(A,CLASSF,N,NREP,TESTFUN)
% R
www.eeworm.com/read/459593/7273118
m multimad.m
function ws = MultiMAD(wc,L)
% MultiMAD -- Apply Shrinkage with level-dependent Noise level estimation
% Usage
% s = MultiMAD(wc,L)
% Inputs
% wc Wavelet Transform of noisy sequence
www.eeworm.com/read/450608/7480158
m normal_map.m
%NORMAL_MAP Map a dataset on normal-density classifiers or mappings
%
% F = NORMAL_MAP(A,W)
%
% INPUT
% A Dataset
% W Mapping
%
% OUTPUT
% F Density estimation for classes in A
%
% DESC
www.eeworm.com/read/449504/7502680
m dcc_mvgarch_full_likelihood.m
function [logL, Rt, likelihoods, Qt]=dcc_garch_full_likelihood(parameters, data, archP,garchQ,dccP,dccQ)
% PURPOSE:
% Full likelihood for use in the DCC_MVGARCH estimation and
% retur
www.eeworm.com/read/449504/7502731
m fattailed_garch.m
function [parameters, likelihood, stderrors, robustSE, ht, scores] = fattailed_garch(data , p , q , errors, startingvals, options)
% PURPOSE:
% FATTAILED_GARCH(P,Q) parameter estimation with dif