  We consider discrete nonparametric priors which induce Gibbs-type
exchangeable random partitions and investigate their posterior behavior in
detail. In particular, we deduce conditional distributions and the
corresponding Bayesian nonparametric estimators, which can be readily exploited
for predicting various features of additional samples. The results provide
useful tools for genomic applications where prediction of future outcomes is
required.
