代码搜索：Probability
找到约 4,670 项符合「Probability」的源代码
代码结果 4,670
www.eeworm.com/read/458010/7314289
m gaussmixp.m
function [m,v,w,g,f,gg,pp,mi,pm]=gaussmix(x,c,l,m0,v0,w0)
%GAUSSMIX fits a gaussian mixture pdf to a set of data observations [m,v,w,g,f]=(x,c,l,m0,v0,w0)
%
% Inputs: n data values, k mixtures, p p
www.eeworm.com/read/451457/7463492
h bitree.h
/*Implements Binary Indexed Trees for cumulative probability tables, based
upon a combination of the techniques described in \cite{Fen93,Fen95,Mof99}.
This is a really, amazingly elegant data str
www.eeworm.com/read/440750/7682272
m gaussmixp.m
function [m,v,w,g,f,gg,pp,mi,pm]=gaussmix(x,c,l,m0,v0,w0)
%GAUSSMIX fits a gaussian mixture pdf to a set of data observations [m,v,w,g,f]=(x,c,l,m0,v0,w0)
%
% Inputs: n data values, k mixtures, p p
www.eeworm.com/read/324404/13264470
m q.m
function [y] = q(x);
% Q-probability function
y = erfc(x / sqrt(2)) / 2;
www.eeworm.com/read/221024/14773401
html http:^^www.cs.cornell.edu^info^people^karr^java^birthday.html
MIME-Version: 1.0
Server: CERN/3.0
Date: Sunday, 01-Dec-96 20:27:08 GMT
Content-Type: text/html
Content-Length: 4188
Last-Modified: Friday, 03-May-96 21:05:01 GMT
Roll Dic
www.eeworm.com/read/393518/8281135
m gaussmixp.m
function [m,v,w,g,f,gg,pp,mi,pm]=gaussmix(x,c,l,m0,v0,w0)
%GAUSSMIX fits a gaussian mixture pdf to a set of data observations [m,v,w,g,f]=(x,c,l,m0,v0,w0)
%
% Inputs: n data values, k mixtures, p p
www.eeworm.com/read/253692/12205245
m main2.m
%本程序是两步式多目标遗传算法。第一步寻找PARETO部分解，第二步寻找PARETO整个边界
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%初始化
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
nind=input('种群大小nind=')
nvar=inp
www.eeworm.com/read/365862/9842918
m gmm_counting_algorithm.m
function w = gmm_counting_algorithm(g, prob, N)
%function w = gmm_counting_algorithm(g, prob, N)
%
% INPUTS:
% g - Gaussian mixture
% prob - probability mass to enclose [0, 1]
% N - numbe
www.eeworm.com/read/351998/10588912
m gmm_counting_algorithm.m
function w = gmm_counting_algorithm(g, prob, N)
%function w = gmm_counting_algorithm(g, prob, N)
%
% INPUTS:
% g - Gaussian mixture
% prob - probability mass to enclose [0, 1]
% N - numbe