  In this paper, we provide an explicit probability distribution for
classification purposes. It is derived from the Bayesian nonparametric mixture
of Dirichlet process model, but with suitable modifications which remove
unsuitable aspects of the classification based on this model. The resulting
approach then more closely resembles a classical hierarchical grouping rule in
that it depends on sums of squares of neighboring values. The proposed
probability model for classification relies on a simulation algorithm which
will be based on a reversible MCMC algorithm for determining the probabilities,
and we provide numerical illustrations comparing with alternative ideas for
classification.
