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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Entry: bart Aliases: bart plot.bart Keywords: nonparametric tree regression nonlinear Description: Bayesian Additive Regression Trees URL: ../../../library/BayesTree/html/bart.html Entry: pdbar
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www.eeworm.com/read/176823/9483080

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/176823/9483191

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/176823/9483236

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/176823/9483237

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/425546/10349078

m demev2.m

%DEMEV2 Demonstrate Bayesian classification for the MLP. % % Description % A synthetic two class two-dimensional dataset X is sampled from a % mixture of four Gaussians. Each class is associated
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www.eeworm.com/read/278889/10490479

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/421949/10676037

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/469416/6976404

m demev2.m

%DEMEV2 Demonstrate Bayesian classification for the MLP. % % Description % A synthetic two class two-dimensional dataset X is sampled from a % mixture of four Gaussians. Each class is associated
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www.eeworm.com/read/469416/6976431

m fevbayes.m

function [extra, invhess] = fevbayes(net, y, a, x, t, x_test, invhess) %FEVBAYES Evaluate Bayesian regularisation for network forward propagation. % % Description % EXTRA = FEVBAYES(NET, Y, A, X,
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