Maximum Likelihood parameter estimation from incomplete data via the sensitivity equations: The continuous-time case
Date
1999Publisher
IEEESource
Proceedings of the American Control ConferenceProceedings of the American Control Conference
Volume
5Pages
3412-3416Google Scholar check
Keyword(s):
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The problem of estimating the parameters for continuous-time partially observed systems is discussed. New exact filters for obtaining Maximum Likelihood (ML) parameter estimates via the Expectation Maximization algorithm are derived. The methodology exploits relations between incomplete and complete data likelihood and gradient of likelihood functions, which are derived using Girsanov's measure transformations. The ML parameter estimates are described by a set of Lyapunov sensitivity equations.