  The paper deals with the problem of designing informative input trajectories
for data-driven simulation. First, the excitation requirements in the case of
noise-free data are discussed and new weaker conditions, which assume the
simulated input to be known in advance, are provided. Then, the case of noisy
data trajectories is considered and an input design problem based on a recently
proposed maximum likelihood estimator is formulated. A Bayesian interpretation
is provided, and the implications of using Hankel and Page matrix
representations are demonstrated. Numerical examples show the impact of the
designed input on the predictive accuracy.
