Maximum likelihood parameter estimation from incomplete data via the sensitivity equations: The continuous-time case

This paper is concerned with maximum likelihood (ML) parameter estimation o f continuous-time nonlinear partially observed stochastic systems, via the expectation maximization (EM) algorithm. It is shown that the EM algorithm can be executed efficiently, provided the unnormalized conditional density of nonlinear filtering is either explicitly solvable or numerically impleme nted. The methodology exploits the relationships between incomplete and com plete data, log likelihood and its gradient.