Bayesian nonparametric estimators derived from conditional Gibbs structures

Abstract

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.

Published in at http://dx.doi.org/10.1214/07-AAP495 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (1)

• Poisson Process Partition Calculus with applications to Exchangeable models and Bayesian Nonparametrics

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