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/213492/15133819
m pdfgauss.m
function y = pdfgauss(X, arg1, arg2 )
% PDFGAUSS Evaluates multivariate Gaussian distribution.
%
% Synopsis:
% y = pdfgauss(X, Mean, Cov)
% y = pdfgauss(X, model )
%
% Description:
% y = pdfgauss(X
www.eeworm.com/read/170937/9779078
m mvar.m
function [ARF,RCF,PE,DC,varargout] = mvar(Y, Pmax, Mode);
% MVAR estimates Multi-Variate AutoRegressive model parameters
% [AR,RC,PE] = mvar(Y, Pmax);
%
% INPUT:
% Y Multivariate data series
% Pmax
www.eeworm.com/read/411674/11233967
m pdfgauss.m
function y = pdfgauss(X, arg1, arg2 )
% PDFGAUSS Evaluates multivariate Gaussian distribution.
%
% Synopsis:
% y = pdfgauss(X, Mean, Cov)
% y = pdfgauss(X, model )
%
% Description:
% y = pdfgauss(X
www.eeworm.com/read/411674/11233974
bak pdfgauss.m.bak
function y = pdfgauss(X, arg1, arg2 )
% PDFGAUSS Evaluates multivariate Gaussian distribution.
%
% Synopsis:
% y = pdfgauss(X, Mean, Cov)
% y = pdfgauss(X, model )
%
% Description:
% y = pdfgauss(X
www.eeworm.com/read/205038/15328570
html rescale.html
rescale
Description of the program: rescale
This program takes a possibly multivariate t
www.eeworm.com/read/375399/9361917
m poly2matrix.m
function [d, h_coeff] = poly2matrix(h)
% [d, h_coeff] = poly2matrix(h)
% convert the filter coefficients into matrix form
% input: h -- analysis filters in the form of multivariate polynami
www.eeworm.com/read/375212/9369156
m pcrg.m
function [t,p,b,ssq,eigs] = pcrg(x,y,pc)
%PCR Principal components regression for multivariate y for use by MODLGUI
% Copyright
% Barry M. Wise
% 1992
% Modified by B.M. Wise, November 1993
www.eeworm.com/read/373627/9445973
html cov.rob.html
R: Resistant Estimation of Multivariate Location and Scatter
www.eeworm.com/read/373627/9445985
html predict.lda.html
R: Classify Multivariate Observations by Linear Discrimination
www.eeworm.com/read/362500/9995914
m mcr.m
function [c,s] = mcr(x,c0,ccon,scon,ittol,cc,sc,sclc,scls,nnlstol);
%MCR Multivariate curve resolution with constraints
% Inputs are (x) the matrix to be decomposed as X = CS, and (c0)
% the init