Electronic Journal of Statistics
- Electron. J. Statist.
- Volume 9, Number 2 (2015), 2884-2902.
A note on nonparametric inference for species variety with Gibbs-type priors
A Bayesian nonparametric methodology has been recently introduced for estimating, given an initial observed sample, the species variety featured by an additional unobserved sample of size $m$. Although this methodology led to explicit posterior distributions under the general framework of Gibbs-type priors, there are situations of practical interest where $m$ is required to be very large and the computational burden for evaluating these posterior distributions makes impossible their concrete implementation. In this paper we present a solution to this problem for a large class of Gibbs-type priors which encompasses the two parameter Poisson-Dirichlet prior and, among others, the normalized generalized Gamma prior. Our solution relies on the study of the large $m$ asymptotic behaviour of the posterior distribution of the number of new species in the additional sample. In particular we introduce a simple characterization of the limiting posterior distribution in terms of a scale mixture with respect to a suitable latent random variable; this characterization, combined with the adaptive rejection sampling, leads to derive a large $m$ approximation of any feature of interest from the exact posterior distribution. We show how to implement our results through a simulation study and the analysis of a dataset in linguistics.
Electron. J. Statist., Volume 9, Number 2 (2015), 2884-2902.
Received: February 2015
First available in Project Euclid: 4 January 2016
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Zentralblatt MATH identifier
Adaptive rejection sampling Bayesian nonparametric inference empirical linguistics Gibbs-type priors normalized generalized Gamma prior species sampling asymptotics two parameter Poisson-Dirichlet prior
Favaro, Stefano; F. James, Lancelot. A note on nonparametric inference for species variety with Gibbs-type priors. Electron. J. Statist. 9 (2015), no. 2, 2884--2902. doi:10.1214/15-EJS1096. https://projecteuclid.org/euclid.ejs/1451916110