- Bayesian Anal.
- Advance publication (2018), 30 pages.
Hierarchical Species Sampling Models
This paper introduces a general class of hierarchical nonparametric prior distributions which includes new hierarchical mixture priors such as the hierarchical Gnedin measures, and other well-known prior distributions such as the hierarchical Pitman-Yor and the hierarchical normalized random measures. The random probability measures are constructed by a hierarchy of generalized species sampling processes with possibly non-diffuse base measures. The proposed framework provides a probabilistic foundation for hierarchical random measures, and allows for studying their properties under the alternative assumptions of diffuse, atomic and mixed base measure. We show that hierarchical species sampling models have a Chinese Restaurants Franchise representation and can be used as prior distributions to undertake Bayesian nonparametric inference. We provide a general sampling method for posterior approximation which easily accounts for non-diffuse base measures such as spike-and-slab.
Bayesian Anal., Advance publication (2018), 30 pages.
First available in Project Euclid: 2 October 2019
Permanent link to this document
Digital Object Identifier
Bassetti, Federico; Casarin, Roberto; Rossini, Luca. Hierarchical Species Sampling Models. Bayesian Anal., advance publication, 2 October 2019. doi:10.1214/19-BA1168. https://projecteuclid.org/euclid.ba/1569981632
- Supplementary material A to Hierarchical Species Sampling Models. This document contains the derivations of the results of the paper and a detailed analysis of the generalized species sampling (with a general base measure). It also describes the Chinese Restaurant Franchise Sampler for Hierarchical Species Sampling Mixtures.
- Supplementary material B to Hierarchical Species Sampling Models. This document provides further numerical illustrations and robustness checks.