Isabelle Braems

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Bounded-error estimation is the estimation of the parameter or state vector of a model from experimental data, under the assumption that some suitably de…ned errors should belong to some prior feasible sets. When the model outputs are linear in the vector to be estimated, a number of methods are available to enclose all estimates that are consistent with(More)
The bounded-error approach to parameter estimation, mainly developed in the context of control and signal processing, is applied in the electrochemistry field in order to obtain reliable estimates for kinetic parameters. The method is based on the assumption that an uncertainty bar is available for each measurement. A set guaranteed to contain all values of(More)
Current plans call for the first Terrestrial Planet Finder mission, TPF-C, to be a monolithic space telescope with a coronagraph for achieving high contrast. The coronagraph removes the diffracted starlight allowing the nearby planet to be detected. In this paper, we present a model of the planet measurement and noise statistics. We utilize this model to(More)
In a bounded-error context, reliable set-inversion algorithms such as Sivia provide guaranteed estimates of the set of all the parameters deemed compatible with the selected model and the collected data, assuming that all the uncertain variables of the model are those to be estimated. In this paper we propose a new approach to estimate the parameters of(More)
  • I Braems, N Ramdaniz, M Kiefferx, L Jaulinô, E Walterx, Y Candauz
  • 2006
A crucial problem that occurs when estimating physical parameters from experimental data is the computation of reliable uncertainty bounds for the estimated parameters, while accounting for uncertainty in the model and data. We introduce a new numerical method that contributes to the solution of this problem. We show how to deal with uncertain nuisance(More)
– This paper introduces new methods for estimating parameters and their uncertainty in the context of inverse problems. The new techniques are capable of dealing with both measurement and modelling errors but also with uncertainty in parameters of the model that are assumed known. All the uncertain quantities are taken as unknown but bounded. In such a(More)
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