A conjugate class of random probability measures based on tilting and with its posterior analysis

Abstract

This article constructs a class of random probability measures based on exponentially and polynomially tilting operated on the laws of completely random measures. The class is proved to be conjugate in that it covers both prior and posterior random probability measures in the Bayesian sense. Moreover, the class includes some common and widely used random probability measures, the normalized completely random measures (James (Poisson process partition calculus with applications to exchangeable models and Bayesian nonparametrics (2002) Preprint), Regazzini, Lijoi and Pr\"{u}nster (Ann. Statist. 31 (2003) 560-585), Lijoi, Mena and Pr\"{u}nster (J. Amer. Statist. Assoc. 100 (2005) 1278-1291)) and the Poisson-Dirichlet process (Pitman and Yor (Ann. Probab. 25 (1997) 855-900), Ishwaran and James (J. Amer. Statist. Assoc. 96 (2001) 161-173), Pitman (In Science and Statistics: A Festschrift for Terry Speed (2003) 1-34 IMS)), in a single construction. We describe an augmented version of the Blackwell-MacQueen P\'{o}lya urn sampling scheme (Blackwell and MacQueen (Ann. Statist. 1 (1973) 353-355)) that simplifies implementation and provide a simulation study for approximating the probabilities of partition sizes.