Probabilistic Models for Bacterial Taxonomy

M. Gyllenberg and T. Koski

International Statistical Review, 2001, vol. 69, issue 2, 249-276

Abstract: We give a survey of different partitioning methods that have been applied to bacterial taxonomy. We introduce a theoretical framework, which makes it possible to treat the various models in a unified way. The key concepts of our approach are prediction and storing of microbiological information in a Bayesian forecasting setting. We show that there is a close connection between classification and probabilistic identification and that, in fact, our approach ties these two concepts together in a coherent way. Nous donnons ici une présentation gén érale des différentesmé méthodes probabilistes de partition appliquéquées à la taxonomic bactérienne, dans un cadre théorique nouveau qui en permet un traitement unifié. Notre approch repose sur une méthode de prévision bayesienne pour la prédiction et le stockage de I; information mecribiologique. Nous montrons que les notions de classification et d' ideutification probabiliste sont étroitement liées et que notre théorie réconcilie ces deux concepts dans une approche chérente.

Date: 2001
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