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Discrete random probability measures: a general framework for nonparametric Bayesian inference

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  • Ongaro, Andrea
  • Cattaneo, Carla

Abstract

A unifying framework for Bayesian analysis in discrete nonparametric settings is proposed. To this aim, a general class of nonparametric discrete prior distributions on an arbitrary sample space is introduced. The general structure of the posterior and predictive distributions and an explicit updating mechanism for the posterior are developed.

Suggested Citation

  • Ongaro, Andrea & Cattaneo, Carla, 2004. "Discrete random probability measures: a general framework for nonparametric Bayesian inference," Statistics & Probability Letters, Elsevier, vol. 67(1), pages 33-45, March.
    • Handle: RePEc:eee:stapro:v:67:y:2004:i:1:p:33-45
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    Cited by:

    1. Yang Liu & Xiaojing Wang, 2020. "Bayesian Nonparametric Monotone Regression of Dynamic Latent Traits in Item Response Theory Models," Journal of Educational and Behavioral Statistics, , vol. 45(3), pages 274-296, June.
    2. Debdeep Pati & David Dunson, 2014. "Bayesian nonparametric regression with varying residual density," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(1), pages 1-31, February.
    3. Björn Bornkamp & Katja Ickstadt, 2009. "Bayesian Nonparametric Estimation of Continuous Monotone Functions with Applications to Dose–Response Analysis," Biometrics, The International Biometric Society, vol. 65(1), pages 198-205, March.
    4. Hatjispyros, Spyridon J. & Merkatas, Christos & Walker, Stephen G., 2023. "Mixture models with decreasing weights," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).

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