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Probabilistic models for bacterial taxonomy
Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Mathematical Statistics .
2001 (English)In: International Statistical Review, ISSN 0306-7734, E-ISSN 1751-5823, Vol. 69, no 2, 249-276 p.Article, review/survey (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2001. Vol. 69, no 2, 249-276 p.
Keyword [en]
clustering, Bayesian statistics, predictive inference, rules of succession, species sampling, machine learning, exchangeability, multivariate Bernoulli distributions
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-48173OAI: oai:DiVA.org:liu-48173DiVA: diva2:269069
Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2017-12-12

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Koski, Timo

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