代码搜索：Probabilities
找到约 751 项符合「Probabilities」的源代码
代码结果 751
www.eeworm.com/read/297150/8050458
java bayesiantrainer.java
import java.io.*;
import java.util.*;
/**
* Use Paul Graham's formula to calculate the probabilities of words indicating SPAM:
*/
public class BayesianTrainer {
Hashtable spamHash = new Has
www.eeworm.com/read/397122/8065750
m bay_optimize.m
function [model,A,B,C,D] = bay_optimize(model,level, type, nb, bay)
% Optimize the posterior probabilities of model (hyper-) parameters with respect to the different levels in Bayesian inference
%
%
www.eeworm.com/read/397102/8068481
m testn.m
%TESTN Error estimate of discriminant for normal distribution.
%
% e = testn(W,U,G,n)
%
% n normally distributed data vectors with means, labels and prior
% probabilities defined by the dataset U
www.eeworm.com/read/246053/12763682
cc generate_seq.cc
// file : generate_seq.cc
// author: Richard Myers
// version: 1.03 [August 21, 1995]
//
// This program generates random sequences using the probabilities defined
// in a markov model file.
#incl
www.eeworm.com/read/331336/12832399
m bay_optimize.m
function [model,A,B,C,D] = bay_optimize(model,level, type, nb, bay)
% Optimize the posterior probabilities of model (hyper-) parameters with respect to the different levels in Bayesian inference
%
%
www.eeworm.com/read/242663/12994205
txt read me.txt
montecarlo
type montecarlo in the command window and wait for a long time..
_simulation of the complete OFDM system.
_use of a very large file in order to get probabilities.
_loop over diffe
www.eeworm.com/read/324303/13273663
m bay_optimize.m
function [model,A,B,C,D] = bay_optimize(model,level, type, nb, bay)
% Optimize the posterior probabilities of model (hyper-) parameters with respect to the different levels in Bayesian inference
%
%
www.eeworm.com/read/318947/13465958
m bay_optimize.m
function [model,A,B,C,D] = bay_optimize(model,level, type, nb, bay)
% Optimize the posterior probabilities of model (hyper-) parameters with respect to the different levels in Bayesian inference
%
%
www.eeworm.com/read/316944/13513992
m bay_optimize.m
function [model,A,B,C,D] = bay_optimize(model,level, type, nb, bay)
% Optimize the posterior probabilities of model (hyper-) parameters with respect to the different levels in Bayesian inference
%
%
www.eeworm.com/read/140847/5779221
m mk_rnd_map_hhmm.m
function bnet = mk_rnd_map_hhmm(varargin)
% We copy the deterministic structure of the real HHMM,
% but randomize the probabilities of the adjustable CPDs.
% The key trick is that 0s in the real