function [B,D,mu] = extract_params_from_gbn(bnet)% Extract all the local parameters of each Gaussian node, and collect them into global matrices.% [B,D,mu] = extract_params_from_gbn(bnet)%% B(i,j) is a block matrix that contains the transposed weight matrix from node i to node j.% D(i,i) is a block matrix that contains the noise covariance matrix for node i.% mu(i) is a block vector that contains the shifted noise mean for node i.% In Shachter's model, the mean of each node in the global gaussian is% the same as the node's local unconditional mean.% In Alag's model (which we use), the global mean gets shifted.num_nodes = length(bnet.dag);bs = bnet.node_sizes(:); % bs = block sizesN = sum(bs); % num scalar nodesB = zeros(N,N);D = zeros(N,N);mu = zeros(N,1);for i=1:num_nodes % in topological order  ps = parents(bnet.dag, i);  e = bnet.equiv_class(i);  %[m, Sigma, weights] = extract_params_from_CPD(bnet.CPD{e});  s = struct(bnet.CPD{e}); % violate privacy of object  m = s.mean; Sigma = s.cov; weights = s.weights;  if length(ps) == 0    mu(block(i,bs)) = m;  else    mu(block(i,bs)) = m + weights *  mu(block(ps,bs));  end  B(block(ps,bs), block(i,bs)) = weights';  D(block(i,bs), block(i,bs)) = Sigma;end