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/284259/8952225
r ldmvnorm.r
### Name: ldmvnorm
### Title: Log Multivariate Normal Density
### Aliases: ldmvnorm
### Keywords: distribution multivariate
### ** Examples
Y
www.eeworm.com/read/355337/10274860
m gauss_rnd.m
%GAUSS_RND Multivariate Gaussian random variables
%
% Syntax:
% X = GAUSS_RND(M,S,N)
%
% In:
% M - Dx1 mean of distibution or K values as DxK matrix.
% S - DxD covariance matrix
% N -
www.eeworm.com/read/355237/10284064
m gauss_rnd.m
%GAUSS_RND Multivariate Gaussian random variables
%
% Syntax:
% X = GAUSS_RND(M,S,N)
%
% In:
% M - Dx1 mean of distibution or K values as DxK matrix.
% S - DxD covariance matrix
% N -
www.eeworm.com/read/355170/10289547
m gauss_rnd.m
%GAUSS_RND Multivariate Gaussian random variables
%
% Syntax:
% X = GAUSS_RND(M,S,N)
%
% In:
% M - Dx1 mean of distibution or K values as DxK matrix.
% S - DxD covariance matrix
% N -
www.eeworm.com/read/333209/7154815
m gauss_rnd.m
%GAUSS_RND Multivariate Gaussian random variables
%
% Syntax:
% X = GAUSS_RND(M,S,N)
%
% In:
% M - Dx1 mean of distibution or K values as DxK matrix.
% S - DxD covariance matrix
% N -
www.eeworm.com/read/303058/13822587
m gauss_rnd.m
%GAUSS_RND Multivariate Gaussian random variables
%
% Syntax:
% X = GAUSS_RND(M,S,N)
%
% In:
% M - Dx1 mean of distibution or K values as DxK matrix.
% S - DxD covariance matrix
% N -
www.eeworm.com/read/407295/11422471
m gauss_rnd.m
%GAUSS_RND Multivariate Gaussian random variables
%
% Syntax:
% X = GAUSS_RND(M,S,N)
%
% In:
% M - Dx1 mean of distibution or K values as DxK matrix.
% S - DxD covariance matrix
% N -
www.eeworm.com/read/407295/11422501
m gauss_rnd.m
%GAUSS_RND Multivariate Gaussian random variables
%
% Syntax:
% X = GAUSS_RND(M,S,N)
%
% In:
% M - Dx1 mean of distibution or K values as DxK matrix.
% S - DxD covariance matrix
% N -
www.eeworm.com/read/251528/12339418
m gauss_rnd.m
%GAUSS_RND Multivariate Gaussian random variables
%
% Syntax:
% X = GAUSS_RND(M,S,N)
%
% In:
% M - Dx1 mean of distibution or K values as DxK matrix.
% S - DxD covariance matrix
% N -