runs Kalman-Bucy filter over observations matrix Z

for 1-step prediction onto matrix X (X can = Z)

with model order p

V = initial covariance of observation sequence noise

returns model parameter estimation sequence A,

sequence of predicted outcomes y_pred

and error matrix Ey (reshaped) for y and Ea for a

along with inovation prob P = P(y_t | D_t-1) = evidence