代码搜索：Bayesian

找到约 1,632 项符合「Bayesian」的源代码

代码结果 1,632
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www.eeworm.com/read/339665/12211157

m demhmc3.m

%DEMHMC3 Demonstrate Bayesian regression with Hybrid Monte Carlo sampling. % % Description % The problem consists of one input variable X and one target variable % T with data generated by sampling X
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www.eeworm.com/read/339665/12211475

m demhmc2.m

%DEMHMC2 Demonstrate Bayesian regression with Hybrid Monte Carlo sampling. % % Description % The problem consists of one input variable X and one target variable % T with data generated by sampling X
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www.eeworm.com/read/339665/12211599

m hbayes.m

function [h, hdata] = hbayes(net, hdata) %HBAYES Evaluate Hessian of Bayesian error function for network. % % Description % H = HBAYES(NET, HDATA) takes a network data structure NET together % the da
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www.eeworm.com/read/339665/12211601

m gbayes.m

function [g, gdata, gprior] = gbayes(net, gdata) %GBAYES Evaluate gradient of Bayesian error function for network. % % Description % G = GBAYES(NET, GDATA) takes a network data structure NET together
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www.eeworm.com/read/150905/12249857

m demhmc3.m

%DEMHMC3 Demonstrate Bayesian regression with Hybrid Monte Carlo sampling. % % Description % The problem consists of one input variable X and one target variable % T with data generated by sampling X
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www.eeworm.com/read/150905/12250438

m demhmc2.m

%DEMHMC2 Demonstrate Bayesian regression with Hybrid Monte Carlo sampling. % % Description % The problem consists of one input variable X and one target variable % T with data generated by sampling X
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www.eeworm.com/read/150905/12250476

m hbayes.m

function [h, hdata] = hbayes(net, hdata) %HBAYES Evaluate Hessian of Bayesian error function for network. % % Description % H = HBAYES(NET, HDATA) takes a network data structure NET together % the da
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www.eeworm.com/read/150905/12250478

m gbayes.m

function [g, gdata, gprior] = gbayes(net, gdata) %GBAYES Evaluate gradient of Bayesian error function for network. % % Description % G = GBAYES(NET, GDATA) takes a network data structure NET together
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www.eeworm.com/read/150749/12267302

m bay_modoutclass.m

function [Pplus, Pmin, bay,model] = bay_modoutClass(model,X,priorpos,type,nb,bay) % Estimate the posterior class probabilities of a binary classifier using Bayesian inference % % >> [Ppos, Pneg] = bay
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www.eeworm.com/read/251529/12339336

cpp covflt.cpp

/* * Bayes++ the Bayesian Filtering Library * See Bayes++.htm for copyright license details * Copyright (c) 2002 Michael Stevens * See accompanying Bayes++.htm for terms and conditions of use. *
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