Generalized Species Sampling Priors With Latent Beta Reinforcements

However, in some applications, exchangeability may not be appropriate. We introduce a novel and probabilistically coherent family of nonexchangeable species sampling sequences characterized by a tractable predictive probability function with weights driven by a sequence of independent Beta random variables.

We compare their theoretical clustering properties with those of the Dirichlet process and the two parameters Poisson–Dirichlet process. The proposed construction provides a complete characterization of the joint process, differently from existing work. We then propose the use of such process as prior distribution in a hierarchical Bayes’ modeling framework, and we describe a Markov chain Monte Carlo sampler for posterior inference.