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