Probabilistic Models for Bacterial Taxonomy |
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Authors: | M. Gyllenberg T. Koski |
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Affiliation: | Department of Mathematics, University of Turku, 20014 Turku, Finland;Department of Mathematics, Linköping Institute of Technology, 58183 Linköping, Sweden |
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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. |
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Keywords: | Clustering Bayesian statistics Predictive inference Rules of succession Species sampling Machine learning Exchangeability Multivariate Bernoulli distributions |
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