代码搜索：Probability

找到约 4,670 项符合「Probability」的源代码

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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 %
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www.eeworm.com/read/304909/13783529

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
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www.eeworm.com/read/303815/13808144

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 %
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www.eeworm.com/read/136165/13870603

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 %
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www.eeworm.com/read/238545/13876345

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 %
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www.eeworm.com/read/146906/5734260

java expectationtest.java

/* * ExpectationTest.java * * Created on October 2, 2002, 5:12 AM */ package Examples.Probability; import Statistics.*; /** * * @author java */ public class ExpectationTest { publi
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m contents.m

% Probability distribution estimation. % % emgmm - Expectation-Maximization Algorithm for GMM. % melgmm - Maximizes Expectation of Log-Likelihood for Gaussian mixture. % mlcgmm - Maximal Li
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m~ contents.m~

% Probability distribution estimation. % % emgmm - Expectation-Maximization Algorithm for GMM. % melgmm - Maximizes Expectation of Log-Likelihood for Gaussian mixture. % mlcgmm - Maximal Li
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www.eeworm.com/read/485544/6552741

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
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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
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