1} = gaussian_CPD(bnet, 1, 'mean', x0, 'cov', V0, 'cov_prior_weight', 0);
bnet.CPD{2} = gaussian_CPD(bnet, 2, 'mean', zeros(Y,1), 'cov', R0, 'weights', C0, ...
			   'clamp_mean', 1, 'cov_prior_weight', 0);
bnet.CPD{3} = gaussian_CPD(bnet, 3, 'mean', zeros(X,1), 'cov', Q0, 'weights', A0, ...
			   'clamp_mean', 1, 'cov_prior_weight', 0);
</pre>
We discuss how to do <a href="#inf">inference</a> and <a href="#learn">learning</a> on this model
below.
(See also
my <a href="../Kalman/kalman.html">Kalman filter t