代码搜索：ESTIMATION

找到约 3,786 项符合「ESTIMATION」的源代码

代码结果 3,786
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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
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www.eeworm.com/read/198546/7928929

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
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www.eeworm.com/read/198546/7929044

m egarchx.m

function [parameters, likelihood, stderrors, robustSE, ht, scores]=egarchX(data,p,o,q,errors, X,options, startingvals); % PURPOSE: % EGARCHX(P,Q) parameter estimation with different error distri
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www.eeworm.com/read/198546/7929047

m fattailed_garch2.m

function [parameters, likelihood] = fattailed_garch2(data , p , q , breakpt , startingvals, options) % PURPOSE: % FATTAILED_GARCH(P,Q) parameter estimation with different error distributions, th
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www.eeworm.com/read/397099/8068874

m bayesian_parameter_est.m

function [mu, sigma] = Bayesian_parameter_est(train_patterns, train_targets, sigma) % Estimate the mean using the Bayesian parameter estimation for Gaussian mixture algorithm % Inputs: % pattern
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www.eeworm.com/read/246803/12704297

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
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www.eeworm.com/read/245941/12770948

m bayesian_parameter_est.m

function [mu, sigma] = Bayesian_parameter_est(train_patterns, train_targets, sigma) % Estimate the mean using the Bayesian parameter estimation for Gaussian mixture algorithm % Inputs: % pattern
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www.eeworm.com/read/144238/12806036

m arburgw.m

function varargout= arburgw( x, p, ventana) %ARBURGW AR parameter estimation via windowed-Burg method. % A = ARBURGW(X,ORDER,WINDOW) returns the polynomial A corresponding to the % AR paramet
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www.eeworm.com/read/330850/12864975

m bayesian_parameter_est.m

function [mu, sigma] = Bayesian_parameter_est(train_patterns, train_targets, sigma) % Estimate the mean using the Bayesian parameter estimation for Gaussian mixture algorithm % Inputs: % pattern
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www.eeworm.com/read/137160/13341952

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
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