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/374698/9388816

m bay_optimize.m

function [model,A,B,C,D] = bay_optimize(model,level, type, nb, bay) % Optimize the posterior probabilities of model (hyper-) parameters with respect to the different levels in Bayesian inference % %
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www.eeworm.com/read/177674/9442425

m errbayes.m

function [e, edata, eprior] = errbayes(net, edata) %ERRBAYES Evaluate Bayesian error function for network. % % Description % E = ERRBAYES(NET, EDATA) takes a network data structure NET together % the
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www.eeworm.com/read/176823/9483132

m errbayes.m

function [e, edata, eprior] = errbayes(net, edata) %ERRBAYES Evaluate Bayesian error function for network. % % Description % E = ERRBAYES(NET, EDATA) takes a network data structure NET together % the
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www.eeworm.com/read/359009/10171224

m aicbic.m

function [AIC , BIC] = aicbic(LLF , numParams , numObs) %AICBIC Akaike and Bayesian information criteria for model order selection. % Given optimized log-likelihood function (LLF) values obtained
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www.eeworm.com/read/356653/10223421

m demo_mc.m

% 1) Extended Kalman Filter (EKF) % 2) Unscented Kalman Filter (UKF) % Copyright (c) Gao Yanan (2004) % Novel approach to nonlinear/non-Gaussian Bayesian state estimation % Bea
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www.eeworm.com/read/356653/10223422

m ins_alignment_unsented.m

% 1) Extended Kalman Filter (EKF) % 2) Unscented Kalman Filter (UKF) % Copyright (c) Gao Yanan (2004) % Novel approach to nonlinear/non-Gaussian Bayesian state estimation % Bea
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www.eeworm.com/read/425546/10349073

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 samplin
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www.eeworm.com/read/278889/10490437

m bay_optimize.m

function [model,A,B,C,D] = bay_optimize(model,level, type, nb, bay) % Optimize the posterior probabilities of model (hyper-) parameters with respect to the different levels in Bayesian inference % %
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www.eeworm.com/read/421949/10676027

m bay_optimize.m

function [model,A,B,C,D] = bay_optimize(model,level, type, nb, bay) % Optimize the posterior probabilities of model (hyper-) parameters with respect to the different levels in Bayesian inference % %
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www.eeworm.com/read/469416/6976327

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 samplin
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