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/413912/11137081
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
www.eeworm.com/read/413912/11137210
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
www.eeworm.com/read/413912/11137258
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
www.eeworm.com/read/413912/11137260
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
www.eeworm.com/read/191800/8421968
cpp testwm.cpp
/*
* testwm.cpp
*
* Test a WinMine-generated Bayesian network by computing the log likelihood
* of the observations in a user-specified test set.
*
* Author: Daniel Lowd
www.eeworm.com/read/177674/9442505
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 wit
www.eeworm.com/read/176823/9483194
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 wit
www.eeworm.com/read/175082/9560906
m mixtureselect.m
function [K,c,z,pi,w] = mixtureSelect(data,maxK,sigma,feedback)
% mixtureSelect : estimate a mixture with unknown K using BIC
% (Bayesian Information Criterion)
% [K,c,z,pi,w] = mixtur
www.eeworm.com/read/167735/9953575
m mixtureselect.m
function [K,c,z,pi,w] = mixtureSelect(data,maxK,sigma,feedback)
% mixtureSelect : estimate a mixture with unknown K using BIC
% (Bayesian Information Criterion)
% [K,c,z,pi,w] = mixtur
www.eeworm.com/read/280604/10304601
m mixtureselect.m
function [K,c,z,pi,w] = mixtureSelect(data,maxK,sigma,feedback)
% mixtureSelect : estimate a mixture with unknown K using BIC
% (Bayesian Information Criterion)
% [K,c,z,pi,w] = mixtur