On the stick–breaking representation of normalized inverse Gaussian priors
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 (in distribution) equivalent, representations. Some of these are convenient if one wishes to thoroughly analyze the theoretical properties of the priors being used, others are more useful for modeling dependence and for addressing computational issues. As for the latter purpose, so–called stick–breaking constructions certainly stand out. In this paper we focus on the recently introduced normalized inverse Gaussian process and provide a completely explicit stick–breaking representation for it. Such a new result is of interest both from a theoretical viewpoint and for statistical practice.
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