Probabilistic models for bacterial taxonomy

• 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.

Nyckelord

clustering

Bayesian statistics

predictive inference

rules of succession

species sampling

machine learning

exchangeability

multivariate Bernoulli distributions

TECHNOLOGY

TEKNIKVETENSKAP

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