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/445364/7596351
m gausspdf.m
function prob = gaussPDF(Data, Mu, Sigma)
%
% This function computes the Probability Density Function (PDF) of a
% multivariate Gaussian represented by means and covariance matrix.
%
% Inputs ---
www.eeworm.com/read/319478/13450994
r2 rmrpc.r2
SECTION 9.3, DATA SET # 2 (MULTIVARIATE)
TWO-SAMPLE MULTI-RESPONSE PERMUTATION COMPARISON
INPUT HAS 12 AND 14 CASES IS S
www.eeworm.com/read/224012/14607906
m gausspdf.m
function prob = gaussPDF(Data, Mu, Sigma)
%
% This function computes the Probability Density Function (PDF) of a
% multivariate Gaussian represented by means and covariance matrix.
%
% Inputs ---
www.eeworm.com/read/374411/9407200
m ksizerot.m
function h = ksizeROT(npd,noIQR)
% "Rule of Thumb" estimate (Silverman)
% Estimate is based on assumptions of Gaussian data and kernel
% Actually the multivariate version in Scott ('92)
% Use
www.eeworm.com/read/198970/7900212
m ksizerot.m
function h = ksizeROT(npd,noIQR)
% "Rule of Thumb" estimate (Silverman)
% Estimate is based on assumptions of Gaussian data and kernel
% Actually the multivariate version in Scott ('92)
% Use
www.eeworm.com/read/266962/6290236
m nfpredatorpraymodel.m
function p = nfPredatorPrayModel
% nfnfPredatorPrayModel - nfPredatorPrayModel constructor
%
% The nfnfPredatorPrayModel defines folloving nonlinear multivariate function
%
% | T*(1-a)*x1 + T*b*x
www.eeworm.com/read/266962/6290241
m nfpredatorpraymodel2.m
function p = nfPredatorPrayModel
% nfnfPredatorPrayModel - nfPredatorPrayModel constructor
%
% The nfnfPredatorPrayModel defines folloving nonlinear multivariate function
%
% | T*(1-a)*x1 + T*b*x
www.eeworm.com/read/480200/6668136
m ksizerot.m
function h = ksizeROT(npd,noIQR)
% "Rule of Thumb" estimate (Silverman)
% Estimate is based on assumptions of Gaussian data and kernel
% Actually the multivariate version in Scott ('92)
% Use
www.eeworm.com/read/393395/2474520
m s_stresscorrelation.m
% this script evaluates the ML estimator of location and scatter under the
% multivariate normal assumption by computing replicability, loss, error, bias and inefficiency
% over a stress-test set of
www.eeworm.com/read/393395/2474524
m mlerecursionfort.m
function [Mu,Sigma] = MleRecursionForT(x,Nu,Tolerance)
% this function computes recursively the ML estimators of location and scatter
% of a multivariate Student t distribution with given degrees o