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/357874/10199118
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/357874/10199127
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/349842/10796669
m bayesian_model_comparison.m
function D = Bayesian_Model_Comparison(train_features, train_targets, Ngaussians, region)
% Classify using the Bayesian model comparison algorithm. This function accepts as inputs
% the maximum nu
www.eeworm.com/read/349842/10796809
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/349842/10796827
m bayesian_parameter_est.m
function [mu, sigma] = Bayesian_parameter_est(train_features, train_targets, sigma, region)
% Estimate the mean using the Bayesian parameter estimation for Gaussian mixture algorithm
% Inputs:
%
www.eeworm.com/read/464885/7061728
pdf bayesian decision theory.pdf
www.eeworm.com/read/464893/7061958
pdf tutorial on bayesian networks.pdf
www.eeworm.com/read/399996/7816621
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/399996/7816819
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/399996/7816845
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