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/449504/7503072

m contents.m

% spatial autoregressive model estimation functions % % beta_prior : construct beta-prior for rho over -1,1 interval % compare_models : An example of model comparison using log marginal poster
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www.eeworm.com/read/448259/7535674

m bispeci.m

function [Bspec,waxis] = bispeci (y,nlag,nsamp, overlap,flag, nfft, wind) %BISPECI Bispectrum estimation using the indirect method. % [Bspec,waxis] = bispeci (y,nlag,segsamp,overlap,flag,nfft, wind
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www.eeworm.com/read/448259/7535677

m bispecd.m

function [Bspec,waxis] = bispecd (y,nfft,wind,nsamp,overlap) %BISPECD Bispectrum estimation using the direct (fft-based) approach. % [Bspec,waxis] = bispecd (y, nfft, wind, segsamp, overlap) % y
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www.eeworm.com/read/446535/7577216

m doa.m

function [spec,theta,bearing] = doa(ymat, dspace, dtheta,nsource,order,delta) %DOA Direction-of-arrival estimation. % [spec,theta] = doa(ymat, dspace, dtheta,nsource,order,delta) % ymat - sensor
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www.eeworm.com/read/446532/7577219

m bispeci.m

function [Bspec,waxis] = bispeci (y,nlag,nsamp, overlap,flag, nfft, wind) %BISPECI Bispectrum estimation using the indirect method. % [Bspec,waxis] = bispeci (y,nlag,segsamp,overlap,flag,nfft, wind
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www.eeworm.com/read/441420/7670641

m k_dd3.m

function k_dd3(sv) %K_DD3 Kalman Filter for Estimation of Ambiguities (with I = 0) % Double differenced code and phase observations % SV is the satellite to be differenced with ref. sat.
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www.eeworm.com/read/437944/7739099

m bispeci.m

function [Bspec,waxis] = bispeci (y,nlag,nsamp, overlap,flag, nfft, wind) %BISPECI Bispectrum estimation using the indirect method. % [Bspec,waxis] = bispeci (y,nlag,segsamp,overlap,flag,nfft, wind
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www.eeworm.com/read/437944/7739111

m doa.m

function [spec,theta,bearing] = doa(ymat, dspace, dtheta,nsource,order,delta) %DOA Direction-of-arrival estimation. % [spec,theta] = doa(ymat, dspace, dtheta,nsource,order,delta) % ymat - sensor
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www.eeworm.com/read/399996/7816646

m minimum_cost.m

function test_targets = Minimum_Cost(train_patterns, train_targets, test_patterns, lambda) % Classify using the minimum error criterion via histogram estimation of the densities % Inputs: % trai
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www.eeworm.com/read/199440/7853191

m contents.m

% HMMBOX, version 3.3, Iead Rezek, Oxford University, February 2001 % Matlab toolbox for Max. Aposteriori estimation of Hidden Markov Models % % (Adapted from Hidden Markov Toolbox % Version 3.2 01-O
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