We present a computationally efficient scheme for parameter estimation of continuous dynamical linear systems given discrete-time noisy observations. The proposed scheme is optimal in the sense that it converges iteratively to the exact Maximum Likelihood estimate, where each iteration increases the likelihood.
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Segal et al. (1987) studied this question.