High-level primitives for recursive maximum likelihood estimation

Bernard Levy 1 Albert Benveniste 1 Ramine Nikoukhah 2
1 AS - Signal Processing and Control
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, INRIA Rennes
Abstract : This paper proposes a high level language constituted of only a few primitives and macros for describing recursive maximum likelihood (ML) estimation algorithms. This language is applicable to estimation problems involving linear Gaussian models, or processes taking values in a finite set. The use of high level primitive allows the development of highly modular ML estimation algorithms based on only few numerical blocks. The primitives, which correspond to the combination of different measurements, the extraction of sufficient statistics and the conversion of the status of a variable from unknown to observed, or vice-versa are first defined for linear Gaussian relations specifying mixed deterministic/stochastic information about the system variables. These primitives are used to define other macros and are illustrated by considering the filtering and smoothing problems for linear descriptor systems. In a second stage, the primitives are extended to finite state processes and are used to implement the Viterbi ML state sequence estimator for a hidden Markov model.
Type de document :
Rapport
[Research Report] RR-2088, INRIA. 1993
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Soumis le : mercredi 24 mai 2006 - 15:50:54
Dernière modification le : vendredi 25 mai 2018 - 12:02:03
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  • HAL Id : inria-00074584, version 1

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Bernard Levy, Albert Benveniste, Ramine Nikoukhah. High-level primitives for recursive maximum likelihood estimation. [Research Report] RR-2088, INRIA. 1993. 〈inria-00074584〉

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