On the Stick-Breaking Representation for Homogeneous NRMIs
Stefano Favaro, Antonio Lijoi, Consuelo Rubina Nava, Bernardo Nipoti, Igor Prünster, Yee Whye Teh · Bayesian Analysis · 2015
In this paper, we consider homogeneous normalized random measures with independent increments (hNRMI), a class of nonparametric priors recently introduced in the literature. Many of their distributional properties are known by now but their stick-breaking representation is missing. Here we display such a representation, which will feature dependent stick-breaking weights, and then derive explicit versions for noteworthy special cases of hNRMI. Posterior characterizations are also discussed. Finally, we devise an algorithm for slice sampling mixture models based on hNRMIs, which relies on the representation we have obtained, and implement it to analyze real data.