The Annals of Applied Probability
- Ann. Appl. Probab.
- Volume 18, Number 4 (2008), 1519-1547.
Bayesian nonparametric estimators derived from conditional Gibbs structures
Antonio Lijoi, Igor Prünster, and Stephen G. Walker
Full-text: Open access
Abstract
We consider discrete nonparametric priors which induce Gibbs-type exchangeable random partitions and investigate their posterior behavior in detail. In particular, we deduce conditional distributions and the corresponding Bayesian nonparametric estimators, which can be readily exploited for predicting various features of additional samples. The results provide useful tools for genomic applications where prediction of future outcomes is required.
Article information
Source
Ann. Appl. Probab. Volume 18, Number 4 (2008), 1519-1547.
Dates
First available in Project Euclid: 21 July 2008
Permanent link to this document
https://projecteuclid.org/euclid.aoap/1216677130
Digital Object Identifier
doi:10.1214/07-AAP495
Mathematical Reviews number (MathSciNet)
MR2434179
Zentralblatt MATH identifier
1142.62333
Subjects
Primary: 62G05: Estimation 62F15: Bayesian inference 60G57: Random measures
Keywords
Bayesian nonparametrics Dirichlet process exchangeable random partitions generalized factorial coefficients generalized gamma process population genetics species sampling models two parameter Poisson–Dirichlet process
Citation
Lijoi, Antonio; Prünster, Igor; Walker, Stephen G. Bayesian nonparametric estimators derived from conditional Gibbs structures. Ann. Appl. Probab. 18 (2008), no. 4, 1519--1547. doi:10.1214/07-AAP495. https://projecteuclid.org/euclid.aoap/1216677130.
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