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/469416/6976432

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 % t
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www.eeworm.com/read/469416/6976433

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 toget
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www.eeworm.com/read/449504/7502165

m ar_g.m

function results = ar_g(y,nlag,ndraw,nomit,prior,start) % PURPOSE: MCMC estimates Bayesian heteroscedastic AR(k) model % imposing stability restrictions using Gibbs sampling % y
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www.eeworm.com/read/440842/7680297

m ar_g.m

function results = ar_g(y,nlag,ndraw,nomit,prior,start) % PURPOSE: MCMC estimates Bayesian heteroscedastic AR(k) model % imposing stability restrictions using Gibbs sampling % y
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www.eeworm.com/read/196932/8040068

m bcmerr.m

function [e,edata,eprior] = bcmerr(net, x, t, exactCov, Xtest) % bcmerr - Error function for Bayesian Committee Machine % % Synopsis: % [e,edata,eprior] = bcmerr(net) % e = bcmerr(net, [], [], Xte
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www.eeworm.com/read/397122/8065777

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/331336/12832438

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/143706/12849481

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/143706/12849740

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/143706/12849816

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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