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On the stick-breaking representation of normalized inverse Gaussian priors

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  • S. Favaro
  • A. Lijoi
  • I. Prünster

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.

Suggested Citation

  • S. Favaro & A. Lijoi & I. Prünster, 2012. "On the stick-breaking representation of normalized inverse Gaussian priors," Biometrika, Biometrika Trust, vol. 99(3), pages 663-674.
    • Handle: RePEc:oup:biomet:v:99:y:2012:i:3:p:663-674
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    File URL: http://hdl.handle.net/10.1093/biomet/ass023
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    Cited by:

    1. Aldo Gardini & Carlo Trivisano & Enrico Fabrizi, 2021. "Bayesian Analysis of ANOVA and Mixed Models on the Log-Transformed Response Variable," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 619-641, June.
    2. Miller, Jeffrey W., 2019. "An elementary derivation of the Chinese restaurant process from Sethuraman’s stick-breaking process," Statistics & Probability Letters, Elsevier, vol. 146(C), pages 112-117.

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