代码搜索：Bayesian
找到约 1,632 项符合「Bayesian」的源代码
代码结果 1,632
www.eeworm.com/read/170936/9779141
m demev1.m
%DEMEV1 Demonstrate Bayesian regression for the MLP.
%
% Description
% The problem consists an input variable X which sampled from a
% Gaussian distribution, and a target variable T generated by compu
www.eeworm.com/read/170936/9779187
m demev3.m
%DEMEV3 Demonstrate Bayesian regression for the RBF.
%
% Description
% The problem consists an input variable X which sampled from a
% Gaussian distribution, and a target variable T generated by compu
www.eeworm.com/read/415313/11076361
m demev1.m
%DEMEV1 Demonstrate Bayesian regression for the MLP.
%
% Description
% The problem consists an input variable X which sampled from a
% Gaussian distribution, and a target variable T generated by compu
www.eeworm.com/read/415313/11076423
m demev3.m
%DEMEV3 Demonstrate Bayesian regression for the RBF.
%
% Description
% The problem consists an input variable X which sampled from a
% Gaussian distribution, and a target variable T generated by compu
www.eeworm.com/read/413912/11137085
m demev1.m
%DEMEV1 Demonstrate Bayesian regression for the MLP.
%
% Description
% The problem consists an input variable X which sampled from a
% Gaussian distribution, and a target variable T generated by compu
www.eeworm.com/read/413912/11137142
m demev3.m
%DEMEV3 Demonstrate Bayesian regression for the RBF.
%
% Description
% The problem consists an input variable X which sampled from a
% Gaussian distribution, and a target variable T generated by compu
www.eeworm.com/read/191800/8422000
cpp ttest.cpp
/*
* ttestwcpp
*
* Compare a WinMine-generated Bayesian network to an NBE model by
* computing the log likelihoods of the observations in a user-specified
* test set. Performs a paired t-test f
www.eeworm.com/read/428451/8867187
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
%
%
www.eeworm.com/read/427586/8931901
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
%
%
www.eeworm.com/read/183445/9158618
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
%
%