贝叶斯网络的matlab实现。可以创建贝叶斯网络、训练模型

				function engine = belprop_inf_engine(fg, max_iter, momentum, tol, maximize)
				
				if nargin < 2, max_iter = length(fg.G); end
				if nargin < 3, momentum = 0; end
				if nargin < 4, tol = 1e-3; end
				if nargin < 5, maximize = 0; end
				
				engine.fgraph = fg;
				engine.max_iter = max_iter;
				engine.momentum = momentum;
				engine.tol = tol;
				engine.maximize = maximize;
				
				% store results computed by enter_evidence here
				ndoms = length(fg.doms);
				nvars = length(fg.vars);
				engine.marginal_domains = cell(1, ndoms);
				
				% to compute the marginal on each variable, we need to know which domain to marginalize
				% so we represent each domain as a bit vector, and compute its (pre-evidence) weight
				engine.dom_weight = [];
				
				% engine.dom_bitv = sparse(ndoms, nvars);
				% ns = fg.node_sizes;
				% for i=1:ndoms
				%   engine.dom_bitv(i, fg.doms{i}) = 1;
				%   engine.dom_weight(i) = prod(ns(fg.doms{i}));
				% end
				
				
				engine = class(engine, 'belprop_inf_engine');