Data di Pubblicazione:
2012
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.
Tipologia CRIS:
03A-Articolo su Rivista
Keywords:
Bayesian nonparametrics; Dirichlet process; Normalized inverse Gaussian process; Random probability measures; Stick-breaking representation.
Elenco autori:
Favaro S.; Lijoi A.; Pruenster I.
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