代码搜索：Bayesian
找到约 1,632 项符合「Bayesian」的源代码
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www.eeworm.com/read/251529/12339401
cpp bayesfltalg.cpp
/*
* Bayes++ the Bayesian Filtering Library
* Copyright (c) 2002 Michael Stevens
* See accompanying Bayes++.htm for terms and conditions of use.
*
* $Id: bayesFltAlg.cpp 566 2006-04-07 20:52:10 +
www.eeworm.com/read/119681/14824500
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/214923/15083048
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/251838/4414453
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/251838/4414454
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/251838/4414664
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/251522/4418824
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/215485/4903415
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/215485/4903416
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/215485/4903626
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