spm_Q.m

function [Q] = spm_Q(a,n,q)
% returns an (n x n) autocorrelation matrix for an AR(p) process
% FORMAT [Q] = spm_Q(a,n,q)
%
% A  - vector pf p AR coefficients
% n  - size of Q
% q  - swtich to return precision [default q = 0]
%__________________________________________________________________________
% spm_Q uses a Yule-Walker device to compute K where:
% 
% y = K*z
% 
% such that y is an AR(n) process generated from an i.i.d innovation 
% z.  This means
% 
% cov(y) = <K*z*z'*K> = K*K'
% 
% If called with q ~= 0, a first order process is assumed when evaluating
% the precision (inverse covariance) matrix; i.e., a = a(1)
%__________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging
 
% Karl Friston
% $Id: spm_Q.m 4230 2011-03-07 20:58:38Z karl $
 
% default
%--------------------------------------------------------------------------
try, q; catch, q = 0; end
 
if q
 
    % compute P (precision)
    %----------------------------------------------------------------------
    A    = [-a(1) (1 + a(1)^2) -a(1)];
    Q    = spdiags(ones(n,1)*A,[-1:1],n,n);
 
else
 
    % compute Q (covariance)
    %----------------------------------------------------------------------
    p    = length(a);
    A    = [1 -a(:)'];
    P    = spdiags(ones(n,1)*A,-[0:p],n,n);
    K    = inv(P);
    K    = K.*(abs(K) > 1e-4);
    Q    = K*K';
    Q    = toeplitz(Q(:,1));
 
end