Bayesian classification methods
Eduardo Gutiérrez‐Peña · 2004
Consider the problem of assigning a class label to a set of unclassified cases. If the set of possible classes is known in advance, this is a problem of supervised classification; if, on the other hand, the set of possible classes is not known, then it becomes a problem of unsupervised classification. In this paper we review some Bayesian approaches to classification, both supervised (e.g. discrimination) and unsupervised (e.g. clustering). For the former case, we also derive a fully Bayesian theoretical rule that can be used as the basis for specific model-based classification rules.