Effective strategies for segmenting data into coherent subsets
Résumé
Automatic segmentation of data into coherent subsets is important in applications as varied as signal processing, bioinformatics and pharmacology. Under this general framework, we investigate the problem of data-driven reconstruction of an unknown, piecewise-constant density function and propose two methods to solve it; the first is directly inspired by the segmentation approach, whereas the second uses a maximum likelihood approach. Motivated by a problem in pharmacometrics, we then introduce a segmentation algorithm which fits into the same general framework and is used for automatically binning data for model assessment purposes.
Origine | Fichiers produits par l'(les) auteur(s) |
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