On the stick-breaking representation of normalized inverse Gaussian priors

S. Favaro, A. Lijoi and I. Prünster

Biometrika, 2012, vol. 99, issue 3, 663-674

Abstract: Random probability measures are the main tool for Bayesian nonparametric inference, with their laws acting as prior distributions. Many well-known priors used in practice admit different, though equivalent, representations. In terms of computational convenience, stick-breaking representations stand out. In this paper we focus on the normalized inverse Gaussian process and provide a completely explicit stick-breaking representation for it. This result is of interest both from a theoretical viewpoint and for statistical practice. Copyright 2012, Oxford University Press.

Date: 2012
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