A New Method for Evaluating the Log-Likelihood Gradient, the Hessian, and the Fisher Information Matrix for Linear Dynamic Systems

Mordechai Segal; Ehud Weinstein

doi:10.1109/18.30995

A New Method for Evaluating the Log-Likelihood Gradient, the Hessian, and the Fisher Information Matrix for Linear Dynamic Systems

Mordechai Segal, Ehud Weinstein

Research output: Contribution to journal › Article › peer-review

43 Scopus citations

Abstract

A new method is presented for evaluating the log-likelihood gradient (score), the Hessian, and the Fisher information matrix (FIM) of the parameters of linear dynamic stochastic systems. The method incorporates the optimal smoothing equations and is, therefore, ideal for simultaneous state estimation and parameter identification.

Original language	English
Pages (from-to)	682-687
Number of pages	6
Journal	IEEE Transactions on Information Theory
Volume	35
Issue number	3
DOIs	https://doi.org/10.1109/18.30995
State	Published - May 1989
Externally published	Yes

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abstract = "A new method is presented for evaluating the log-likelihood gradient (score), the Hessian, and the Fisher information matrix (FIM) of the parameters of linear dynamic stochastic systems. The method incorporates the optimal smoothing equations and is, therefore, ideal for simultaneous state estimation and parameter identification.",

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A New Method for Evaluating the Log-Likelihood Gradient, the Hessian, and the Fisher Information Matrix for Linear Dynamic Systems

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