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/119681/14824440
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
www.eeworm.com/read/220289/14843728
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/220289/14843800
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/220289/14843827
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/220289/14843828
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/207287/15002220
txt read_me.txt
Some brief notes
----------------
kfdemo.m is a Matlab script file to run a demonstration of the
Bayesian Kalman filter. It loads file kfdemo.mat (saved as version 4
so that it will read in eit
www.eeworm.com/read/214923/15082919
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
www.eeworm.com/read/212307/15160035
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/212307/15160107
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/212307/15160135
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