Enriched Pitman–Yor processes
Tommaso Rigon, Sonia Petrone, Bruno Scarpa · Scandinavian Journal of Statistics · 2025
Bayesian nonparametrics has evolved into a broad area encompassing flexible methods for Bayesian inference, combinatorial structures, tools for complex data reduction, and more. Discrete prior laws play an important role in these developments, and various choices are available nowadays. However, many existing priors, such as the Dirichlet process, have limitations if data require nested clustering structures. Thus, we introduce a discrete nonparametric prior, termed the enriched Pitman-Yor process, which offers higher flexibility in modeling such elaborate partition structures. We investigate the theoretical properties of this novel prior and establish its formal connection with the enriched Dirichlet process and normalized random measures. Additionally, we present a square-breaking representation and derive closed-form expressions for the posterior law and associated urn schemes. Furthermore, we demonstrate that several established models, including Dirichlet processes with a spike-and-slab base measure and mixture of mixtures models, emerge as special instances of the enriched Pitman-Yor process, which therefore serves as a unified probabilistic framework for various Bayesian nonparametric priors. To illustrate its practical utility, we employ the enriched Pitman-Yor process for a species-sampling ecological problem.