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.

翻译：詹姆斯、利乔伊与普伦斯特（2009）提出的贝叶斯非参数方法，用于基于独立增量归一化随机测度预测观测值的未来取值。本文对该方法进行修正，使其适用于基于负二项过程的模型类——该模型中增量虽非独立，但在以潜在伽马变量为条件时相互独立。与詹姆斯等人的方法类似，新算法通过两个变量构建：其一为历史观测值的函数，其二基于新观测值进行更新。我们概述了该程序在群体遗传学中的应用，即根据等位基因样本构建基因谱系树与溯祖过程的实现。