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A Class of Normalized Random Measures with an Exact Predictive Sampling Scheme

Authors


Lorenzo Trippa, Department of Bíostatistics, DFCI Biostats & Comp Biology, 44 Binney Street, Boston, Massachusetts 02115, USA.
E-mail: ltrippa@jimmy.harvard.edu

Abstract

Abstract.  In this article, we define and investigate a novel class of non-parametric prior distributions, termed the class inline image. Such class of priors is dense with respect to the homogeneous normalized random measures with independent increments and it is characterized by a richer predictive structure than those arising from other widely used priors. Our interest in the class inline image is mainly motivated by Bayesian non-parametric analysis of some species sampling problems concerning the evaluation of the species relative abundances in a population. We study both the probability distribution of the number of species present in a sample and the probability of discovering a new species conditionally on an observed sample. Finally, by using the coupling from the past method, we provide an exact sampling scheme for the system of predictive distributions characterizing the class inline image.

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