Alv-Arne Grimstad

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Uncertainty assessment for parameter estimation problems is considered. It is customary to use confidence regions or intervals to give the precision of the parameter estimates. The method most used, because of modest computational requirements, is linearized covariance analysis. Monte Carlo analysis can be used to check the validity of the linearization for(More)
Application of the conventional X.500 approach to naming has heretofore, in the experience of the authors, proven to be an obstacle to the wide deployment of directory-enabled applications on the Internet. We propose a new directory naming plan that leverages the strengths of the most popular and successful Internet naming schemes for naming objects in a(More)
Uncertainty assessment for parameter estimation problems is considered. It is customary to use con dence regions or intervals to give the precision of the parameter estimates. The method most used, because of modest computational requirements, is linearized covariance analysis. Monte Carlo analysis can be used to check the validity of the linearization for(More)
Both sensitivity and nonlinearity are of importance for the e ciency of an estimation algorithm. Hence, knowledge of a general nature on sensitivity and/or nonlinearity for some class of models can perhaps be utilized to improve the estimation e ciency for this class. For an ODE model, a correlation between high nonlinearity, low sensitivity, and smallscale(More)
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