贝叶斯网络的matlab实现。可以创建贝叶斯网络、训练模型

				% function [L,Ph,LL]=ffa(X,K,cyc,tol);
				% 
				% Fast Maximum Likelihood Factor Analysis using EM
				%
				% X - data matrix
				% K - number of factors
				% cyc - maximum number of cycles of EM (default 100)
				% tol - termination tolerance (prop change in likelihood) (default 0.0001)
				%
				% L - factor loadings 
				% Ph - diagonal uniquenesses matrix
				% LL - log likelihood curve
				%
				% Iterates until a proportional change < tol in the log likelihood 
				% or cyc steps of EM 
				%
				
				function [L,Ph,LL]=ffa(X,K,cyc,tol);
				
				if nargin				if nargin				
				N=length(X(:,1));
				D=length(X(1,:));
				tiny=exp(-700);
				
				X=X-ones(N,1)*mean(X);
				XX=X'*X/N;
				diagXX=diag(XX);
				
				randn('seed', 0);
				cX=cov(X);
				scale=det(cX)^(1/D);
				L=randn(D,K)*sqrt(scale/K);
				Ph=diag(cX);
				
				I=eye(K);
				
				lik=0; LL=[];
				
				const=-D/2*log(2*pi);
				
				
				for i=1:cyc;
				
				  %%%% E Step %%%%
				  Phd=diag(1./Ph);
				  LP=Phd*L;
				  MM=Phd-LP*inv(I+L'*LP)*LP';
				  dM=sqrt(det(MM));
				  beta=L'*MM;
				  XXbeta=XX*beta';
				  EZZ=I-beta*L +beta*XXbeta;
				
				  %%%% Compute log likelihood %%%%
				  
				  oldlik=lik;
				  lik=N*const+N*log(dM)-0.5*N*sum(diag(MM*XX));
				  fprintf('cycle %i lik %g \n',i,lik);
				  LL=[LL lik];
				  
				  %%%% M Step %%%%
				
				  L=XXbeta*inv(EZZ);
				  Ph=diagXX-diag(L*XXbeta');
				
				  if (i				    likbase=lik;
				  elseif (lik				    disp('VIOLATION');
				  elseif ((lik-likbase)				    break;
				  end;
				
				end