代码搜索：multivariate
找到约 564 项符合「multivariate」的源代码
代码结果 564
www.eeworm.com/read/114454/15052874
m arfit.m
function [w, A, C, sbc, fpe, th]=arfit(v, pmin, pmax, selector, no_const)
%ARFIT Stepwise least squares estimation of multivariate AR model.
%
% [w,A,C,SBC,FPE,th]=ARFIT(v,pmin,pmax) produces esti
www.eeworm.com/read/13871/284292
m student_t_logprob.m
function L = log_student_pdf(X, mu, lambda, alpha)
% LOG_STUDENT_PDF Evaluate the log of the multivariate student-t distribution at a point
% L = log_student_pdf(X, mu, lambda, alpha)
%
% Each col
www.eeworm.com/read/210624/4948294
svn-base klmgamma.m.svn-base
%KLMGAMMA the Kullback-Liebler (KL) distance between multivariate
% gamma PDF's
% D = KLMGAMMA(A1,A2,B1,B2) where
%
% A1 is the alpha (Shape) hyper parameter for independant samples
% in th
www.eeworm.com/read/299916/3849573
m multi_gp.m
function [x] = multi_gp(m,C)
% [x]=multi_gp(m,C)
% MULTI_GP generates a multivariate Gaussian random
% process with mean vector m (column vector), and covariance matrix C.
N=length(m);
for
www.eeworm.com/read/273525/4208191
hlp y_mvclush.hlp
{smcl}
{p 0 4}
{help contents:Top}
> {help y_stat:Statistics}
> {help y_mv:Multivariate analysis}
> {help y_mvclus:Cluster analysis}
{bind:> {bf:Hierarchical clustering}}
{p_end}
{hline}
{t
www.eeworm.com/read/273525/4208335
hlp y_mvclus.hlp
{smcl}
{p 0 4}
{help contents:Top}
> {help y_stat:Statistics}
> {help y_mv:Multivariate analysis}
{bind:> {bf:Cluster analysis}}
{p_end}
{hline}
{title:Help and category listings}
{p 4 8
www.eeworm.com/read/396844/2407677
m log_student_pdf.m
function L = log_student_pdf(X, mu, lambda, alpha)
% LOG_STUDENT_PDF Evaluate the log of the multivariate student-t distribution at a point
% L = log_student_pdf(X, mu, lambda, alpha)
%
% Each column
www.eeworm.com/read/396844/2407871
m arfit.m
function [w, A, C, sbc, fpe, th]=arfit(v, pmin, pmax, selector, no_const)
%ARFIT Stepwise least squares estimation of multivariate AR model.
%
% [w,A,C,SBC,FPE,th]=ARFIT(v,pmin,pmax) produces estimat
www.eeworm.com/read/392854/8322607
m student_t_logprob.m
function L = log_student_pdf(X, mu, lambda, alpha)
% LOG_STUDENT_PDF Evaluate the log of the multivariate student-t distribution at a point
% L = log_student_pdf(X, mu, lambda, alpha)
%
% Each column
www.eeworm.com/read/170937/9779034
m mvaar.m
function [x,e,Kalman,Q2] = mvaar(y,p,UC,mode,Kalman)
% Multivariate (Vector) adaptive AR estimation base on a multidimensional
% Kalman filer algorithm. A standard VAR model (A0=I) is implemented. T