wolffd@0: % Test whether stable conditional Gaussian inference works
wolffd@0: % Make a linear dynamical system
wolffd@0: %   X1 -> X2
wolffd@0: %   |     | 
wolffd@0: %   v     v
wolffd@0: %   Y1    Y2 
wolffd@0: 
wolffd@0: intra = zeros(2);
wolffd@0: intra(1,2) = 1;
wolffd@0: inter = zeros(2);
wolffd@0: inter(1,1) = 1;
wolffd@0: n = 2;
wolffd@0: 
wolffd@0: X = 2; % size of hidden state
wolffd@0: Y = 2; % size of observable state
wolffd@0: 
wolffd@0: ns = [X Y];
wolffd@0: bnet = mk_dbn(intra, inter, ns, 'discrete', [], 'observed', 2);
wolffd@0: 
wolffd@0: x0 = rand(X,1);
wolffd@0: V0 = eye(X);
wolffd@0: C0 = rand(Y,X);
wolffd@0: R0 = eye(Y);
wolffd@0: A0 = rand(X,X);
wolffd@0: Q0 = eye(X);
wolffd@0: 
wolffd@0: bnet.CPD{1} = gaussian_CPD(bnet, 1, 'mean', x0, 'cov', V0);
wolffd@0: bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', zeros(Y,1), 'cov', R0, 'weights', C0);
wolffd@0: bnet.CPD{3} = gaussian_CPD(bnet, 3, 'mean', zeros(X,1), 'cov', Q0, 'weights', A0);
wolffd@0: 
wolffd@0: 
wolffd@0: T = 5; % fixed length sequences
wolffd@0: 
wolffd@0: engine = {};
wolffd@0: engine{end+1} = kalman_inf_engine(bnet);
wolffd@0: engine{end+1} = scg_unrolled_dbn_inf_engine(bnet, T);
wolffd@0: engine{end+1} = jtree_unrolled_dbn_inf_engine(bnet, T);
wolffd@0: 
wolffd@0: inf_time = cmp_inference_dbn(bnet, engine, T, 'check_ll', 0);