Exact filters for Newton-Raphson parameter estimation algorithms for continuous-time partially observed stochastic systems

DOI10.1016/S0167-6911(00)00082-7zbMath0985.93058OpenAlexW2180660007WikidataQ126865870 ScholiaQ126865870MaRDI QIDQ5940773

Joseph L. Hibey, Charalambos D. Charalambous

Publication date: 20 August 2001

Published in: Systems \& Control Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-6911(00)00082-7

zbMATH Keywords

scoringparameter estimationHessianKalman filterFisher informationlog-likelihoodNewton-Raphsonstochastic systems

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Identification in stochastic control theory (93E12)

Related Items (1)

EM-based identification of continuous-time ARMA models from irregularly sampled data

Uses Software

• astsa

Cites Work

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• Parameter estimation of partially observed continuous time stochastic processes via the EM algorithm
• On computing the expected Fisher information matrix for state-space model parameters
• A new method for evaluating the log-likelihood gradient, the Hessian, and the Fisher information matrix for linear dynamic systems
• Minimum principle for partially observable nonlinear risk-sensitive control problems using measure-valued decompositions
• Exact Finite-Dimensional Filters for Maximum Likelihood Parameter Estimation of Continuous-time Linear Gaussian Systems
• Classes of Nonlinear Partially Observable Stochastic Optimal Control Problems with Explicit Optimal Control Laws

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