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Clustering dynamics in a class of normalised generalised gamma dependent priors

Matteo Ruggiero () and Matteo Sordello ()
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Matteo Ruggiero: University of Torino
Matteo Sordello: University of Pennsylvania

Annals of the Institute of Statistical Mathematics, 2018, vol. 70, issue 1, No 4, 83-98

Abstract: Abstract Normalised generalised gamma processes are random probability measures that induce nonparametric prior distributions widely used in Bayesian statistics, particularly for mixture modelling. We construct a class of dependent normalised generalised gamma priors induced by a stationary population model of Moran type, which exploits a generalised Pólya urn scheme associated with the prior. We study the asymptotic scaling for the dynamics of the number of clusters in the sample, which in turn provides a dynamic measure of diversity in the underlying population. The limit is formalised to be a positive non-stationary diffusion process which falls outside well-known families, with unbounded drift and an entrance boundary at the origin. We also introduce a new class of stationary positive diffusions, whose invariant measures are explicit and have power law tails, which approximate weakly the scaling limit.

Keywords: Alpha diversity; Bayesian nonparametrics; Dependent process; Diffusion process; Generalised Pólya urn; Moran model; Scaling limit (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10463-016-0583-8

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