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/197905/5090861
m model_select2.m
% Online Bayesian model selection demo.
% We generate data from the model A->B
% and compute the posterior prob of all 3 dags on 2 nodes:
% (1) A B, (2) A B
% Models 2 and 3 are Mar
www.eeworm.com/read/197905/5090862
m model_select1.m
% Bayesian model selection demo.
% We generate data from the model A->B
% and compute the posterior prob of all 3 dags on 2 nodes:
% (1) A B, (2) A B
% Models 2 and 3 are Markov equ
www.eeworm.com/read/197905/5091072
m mk_bat_dbn.m
function [bnet, names] = mk_bat_dbn()
% MK_BAT_DBN Make the BAT DBN
% [bnet, names] = mk_bat_dbn()
% See
% - Forbes, Huang, Kanazawa and Russell, "The BATmobile: Towards a Bayesian Automated Taxi", IJ
www.eeworm.com/read/346158/3189447
m model_select2.m
% Online Bayesian model selection demo.
% We generate data from the model A->B
% and compute the posterior prob of all 3 dags on 2 nodes:
% (1) A B, (2) A B
% Models 2 and 3 are Mar
www.eeworm.com/read/346158/3189448
m model_select1.m
% Bayesian model selection demo.
% We generate data from the model A->B
% and compute the posterior prob of all 3 dags on 2 nodes:
% (1) A B, (2) A B
% Models 2 and 3 are Markov equ
www.eeworm.com/read/346158/3189658
m mk_bat_dbn.m
function [bnet, names] = mk_bat_dbn()
% MK_BAT_DBN Make the BAT DBN
% [bnet, names] = mk_bat_dbn()
% See
% - Forbes, Huang, Kanazawa and Russell, "The BATmobile: Towards a Bayesian Automated Taxi", IJ
www.eeworm.com/read/344585/3207950
m bay_lssvmard.m
function [inputs,ordered,costs,sig2n,model] = bay_lssvmARD(model,type,btype,nb);
% Bayesian Automatic Relevance Determination of the inputs of an LS-SVM
%
%
% >> dimensions = bay_lssvmARD({X,Y,type,
www.eeworm.com/read/292984/3935687
m model_select2.m
% Online Bayesian model selection demo.
% We generate data from the model A->B
% and compute the posterior prob of all 3 dags on 2 nodes:
% (1) A B, (2) A B
% Models 2 and 3 are Mar
www.eeworm.com/read/292984/3935688
m model_select1.m
% Bayesian model selection demo.
% We generate data from the model A->B
% and compute the posterior prob of all 3 dags on 2 nodes:
% (1) A B, (2) A B
% Models 2 and 3 are Markov equ
www.eeworm.com/read/292984/3935898
m mk_bat_dbn.m
function [bnet, names] = mk_bat_dbn()
% MK_BAT_DBN Make the BAT DBN
% [bnet, names] = mk_bat_dbn()
% See
% - Forbes, Huang, Kanazawa and Russell, "The BATmobile: Towards a Bayesian Automated Taxi", IJ