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/397099/8068759
m bayesian_model_comparison.m
function test_targets = Bayesian_Model_Comparison(train_patterns, train_targets, test_patterns, Ngaussians)
% Classify using the Bayesian model comparison algorithm. This function accepts as inputs
www.eeworm.com/read/397099/8068854
m bayesian_belief_networks.m
function [decision, P] = Bayesian_Belief_Networks(net, data)
% Find the most likely decision given a Bayesian belief network and data for the decision
%
% Inputs:
% net - A bayesian belief n
www.eeworm.com/read/397099/8068874
m bayesian_parameter_est.m
function [mu, sigma] = Bayesian_parameter_est(train_patterns, train_targets, sigma)
% Estimate the mean using the Bayesian parameter estimation for Gaussian mixture algorithm
% Inputs:
% pattern
www.eeworm.com/read/245941/12770766
m bayesian_model_comparison.m
function test_targets = Bayesian_Model_Comparison(train_patterns, train_targets, test_patterns, Ngaussians)
% Classify using the Bayesian model comparison algorithm. This function accepts as inputs
www.eeworm.com/read/245941/12770924
m bayesian_belief_networks.m
function [decision, P] = Bayesian_Belief_Networks(net, data)
% Find the most likely decision given a Bayesian belief network and data for the decision
%
% Inputs:
% net - A bayesian belief n
www.eeworm.com/read/245941/12770948
m bayesian_parameter_est.m
function [mu, sigma] = Bayesian_parameter_est(train_patterns, train_targets, sigma)
% Estimate the mean using the Bayesian parameter estimation for Gaussian mixture algorithm
% Inputs:
% pattern
www.eeworm.com/read/330850/12864748
m bayesian_model_comparison.m
function test_targets = Bayesian_Model_Comparison(train_patterns, train_targets, test_patterns, Ngaussians)
% Classify using the Bayesian model comparison algorithm. This function accepts as inputs
www.eeworm.com/read/330850/12864947
m bayesian_belief_networks.m
function [decision, P] = Bayesian_Belief_Networks(net, data)
% Find the most likely decision given a Bayesian belief network and data for the decision
%
% Inputs:
% net - A bayesian belief n
www.eeworm.com/read/330850/12864975
m bayesian_parameter_est.m
function [mu, sigma] = Bayesian_parameter_est(train_patterns, train_targets, sigma)
% Estimate the mean using the Bayesian parameter estimation for Gaussian mixture algorithm
% Inputs:
% pattern
www.eeworm.com/read/317622/13500823
m bayesian_model_comparison.m
function test_targets = Bayesian_Model_Comparison(train_patterns, train_targets, test_patterns, Ngaussians)
% Classify using the Bayesian model comparison algorithm. This function accepts as inputs