  A Bayesian nonparametric method of James, Lijoi \& Prunster (2009) used to
predict future values of observations from normalized random measures with
independent increments is modified to a class of models based on negative
binomial processes for which the increments are not independent, but are
independent conditional on an underlying gamma variable. Like in James et al.,
the new algorithm is formulated in terms of two variables, one a function of
the past observations, and the other an updating by means of a new observation.
We outline an application of the procedure to population genetics, for the
construction of realisations of genealogical trees and coalescents from samples
of alleles.
