代码搜索：Multivariate Analysis
找到约 10,000 项符合「Multivariate Analysis」的源代码
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www.eeworm.com/read/449504/7502477
m mtd_print.m
function []=mtd_print(res,file_sal)
% PURPOSE: Save output of multivariate temporal disaggregation methods
% ------------------------------------------------------------
% SYNTAX: []=mtd_print(res,
www.eeworm.com/read/444270/7615422
m ml_gaussian.m
function index = ML_gaussian(x,mu,sigma)
% function index = ML_gaussian(x,mu,sigma)
% x is a vector drawn from some multivariate gaussian
% mu(i,:) is the mean of the ith Gaussian
% sigma(:,:,i) i
www.eeworm.com/read/483033/6607855
m gmm_rnd.m
%GMM_RND RND from Multivariate Mixture of Gaussians
%
% Syntax:
% X = GMM_RND(M,S,PJ,N)
%
% Author:
% Simo S鋜kk
www.eeworm.com/read/343227/11962856
m gauslogv.m
function logdens = gauslogv(X, mu, Sigma, QUIET)
%gauslogv Computes a set of multivariate normal log-density values.
% Use : logdens = gauslogv(X, mu, Sigma) where
% X (T,p) T observed vectors of d
www.eeworm.com/read/343227/11962913
c c_dgaus.c
/************************************************************************/
/* c_dgaus Mex-file for computing a set of multivariate normal */
/* density values in the case of diag
www.eeworm.com/read/223154/14651884
txt datatype.txt
SPECIFICATION OF DATATYPES
-----------------------------------------------
This is used by PLOTA.M
Date: 4 Apr 2003
(C) Alois Schloegl
Version 1.0 (0.10 for testing)
www.eeworm.com/read/216806/14991767
m predict.m
function particle= predict(particle, V,G,Q, WB,dt, addrandom)
%
% add random noise to controls
if addrandom == 1
VG= multivariate_gauss([V;G], Q, 1);
V= VG(1); G= VG(2);
end
% predi
www.eeworm.com/read/393504/8281485
m predict.m
function particle= predict(particle, V,G,Q, WB,dt, addrandom)
%
% add random noise to controls
if addrandom == 1
VG= multivariate_gauss([V;G], Q, 1);
V= VG(1); G= VG(2);
end
% predi
www.eeworm.com/read/237675/13939024
c loglikelihood.c
/* loglikelihood.c
Compute the loglikelihood of mixture of Multivariate Gaussian pdf.
usage: logl = loglikelihood(Z , mu , sigma , p);
where
Z : measure (d x
www.eeworm.com/read/200237/15437070
m ml_gaussian.m
function index = ML_gaussian(x,mu,sigma)
% function index = ML_gaussian(x,mu,sigma)
% x is a vector drawn from some multivariate gaussian
% mu(i,:) is the mean of the ith Gaussian
% sigma(:,:,i) i