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/485544/6552711
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/485544/6552738
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/485544/6552739
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/480211/6668270
svn-base schools_model.txt.svn-base
# Bugs model file for 8 schools analysis from Section 5.5 of "Bayesian Data
# Analysis". Save this into the file "schools.bug" in your R working directory.
model {
for (j in 1:J){
www.eeworm.com/read/480211/6668307
txt schools_model.txt
# Bugs model file for 8 schools analysis from Section 5.5 of "Bayesian Data
# Analysis". Save this into the file "schools.bug" in your R working directory.
model {
for (j in 1:J){
www.eeworm.com/read/253950/12173283
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/253950/12173510
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/253950/12173598
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/253950/12173601
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