  We propose an input design method for a general class of parametric
probabilistic models, including nonlinear dynamical systems with process noise.
The goal of the procedure is to select inputs such that the parameter posterior
distribution concentrates about the true value of the parameters; however,
exact computation of the posterior is intractable. By representing (samples
from) the posterior as trajectories from a certain Hamiltonian system, we
transform the input design task into an optimal control problem. The method is
illustrated via numerical examples, including MRI pulse sequence design.

Systems and Control;Optimization and Control;Machine Learning