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Probabilistic Models for Bacterial Taxonomy
Author(s) -
Gyllenberg M.,
Koski T.
Publication year - 2001
Publication title -
international statistical review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.051
H-Index - 54
eISSN - 1751-5823
pISSN - 0306-7734
DOI - 10.1111/j.1751-5823.2001.tb00458.x
Subject(s) - probabilistic logic , taxonomy (biology) , computer science , key (lock) , connection (principal bundle) , identification (biology) , artificial intelligence , bayesian probability , data mining , machine learning , mathematics , ecology , biology , geometry , computer security
Summary 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.