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Reports (Research Report) Year : 1993

High-level primitives for recursive maximum likelihood estimation

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.

Domains

Other [cs.OH]
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Dates and versions

inria-00074584 , version 1 (24-05-2006)

Identifiers

  • HAL Id : inria-00074584 , version 1

Cite

Bernard C. 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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