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/286656/4038392
m compare.m
% Compare asexual reproduction and sexual reproduction
clear all
clc
BOAT_NUM = 100; % Number of individuals
ROWER = 100; % Precision of variables
Pc= 0.5; % the probability of crossover
Pm
www.eeworm.com/read/286656/4038393
asv compare.asv
% Compare asexual reproduction and sexual reproduction
clear all
clc
BOAT_NUM = 100; % Number of individuals
ROWER = 20; % Precision of variables
Pc= 0.5; % the probability of crossover
Pm=
www.eeworm.com/read/286656/4038407
m compare.m
% Compare asexual reproduction and sexual reproduction
clear all
clc
BOAT_NUM = 100; % Number of individuals
ROWER = 100; % Precision of variables
Pc= 0.5; % the probability of crossover
Pm
www.eeworm.com/read/286656/4038408
asv compare.asv
% Compare asexual reproduction and sexual reproduction
clear all
clc
BOAT_NUM = 100; % Number of individuals
ROWER = 20; % Precision of variables
Pc= 0.5; % the probability of crossover
Pm=
www.eeworm.com/read/273525/4204502
ihlp f_gammaden.ihlp
{* 02feb2005}{...}
{phang}
{cmd:gammaden(}{it:a}{cmd:,}{it:b}{cmd:,}{it:g}{cmd:,}{it:x}{cmd:)}
returns the probability density function of the Gamma distribution,
where {cmd:gammaden(}{it
www.eeworm.com/read/396844/2406682
m gmmprob.m
function prob = gmmprob(mix, x)
%GMMPROB Computes the data probability for a Gaussian mixture model.
%
% Description
% This function computes the unconditional data density P(X) for a
% Gaussian mixt
www.eeworm.com/read/396844/2406687
m gmmpost.m
function [post, a] = gmmpost(mix, x)
%GMMPOST Computes the class posterior probabilities of a Gaussian mixture model.
%
% Description
% This function computes the posteriors POST (i.e. the probability
www.eeworm.com/read/378175/2689913
m multinonunifmutation.m
function [parent] = multiNonUnifMutation(parent,bounds,Ops)
% Multi-Non uniform mutation changes all of the parameters of the parent
% based on a non-uniform probability distribution. This Gaussian
%
www.eeworm.com/read/376881/2706664
m contents.m
% Probability distribution estimation.
%
% emgmm - Expectation-Maximization Algorithm for GMM.
% melgmm - Maximizes Expectation of Log-Likelihood for Gaussian mixture.
% mlcgmm - Maximal Li
www.eeworm.com/read/376881/2706672
m~ contents.m~
% Probability distribution estimation.
%
% emgmm - Expectation-Maximization Algorithm for GMM.
% melgmm - Maximizes Expectation of Log-Likelihood for Gaussian mixture.
% mlcgmm - Maximal Li