wolffd@0: function [coef, C] = learn_AR_diagonal(y, k)
wolffd@0: % Find the ML parameters for a collection of independent scalar AR processes.
wolffd@0: 
wolffd@0: % sep_coef(1,1,t,i) is the coefficient to apply to compopnent i of the state vector t steps ago
wolffd@0: % eg. consider two components L and R and let A = coef(:,:,1,:), B = coef(:,:,2,:)
wolffd@0: % L3    (AL  0   BL 0)     (L2)   (CL 0 0 0)
wolffd@0: % R3 =  (0   AR  0  BR)    (R2)   (0 CR 0 0)
wolffd@0: % L2    (1   0   0  0 )    (L1) + (0 0  0 0)
wolffd@0: % R2    (0   1   0  0 )    (R1)   (0 0  0 0)
wolffd@0: 
wolffd@0: ss = size(y, 1);
wolffd@0: sep_coef = zeros(1,1,k,ss);
wolffd@0: for i=1:ss
wolffd@0:   [sep_coef(:,:,:,i), sep_cov(i)] = learn_AR(k, y(i,:));
wolffd@0: end
wolffd@0: C = diag(sep_cov);
wolffd@0: for t=1:k
wolffd@0:   x = sep_coef(1,1,t,:);
wolffd@0:   coef(:,:,t) = diag(x(:));
wolffd@0: end