Work

Bayesian nonparametric estimators derived from conditional Gibbs structures

Year: 2008

Type: article

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.

Source: The Annals of Applied Probability

DOI: 10.1214/07-aap495

Authors: Antonio Lijoi, Igor Prünster, Stephen G. Walker

Institutions: Istituto di Matematica Applicata e Tecnologie Informatiche, Collegio Carlo Alberto, University of Pavia, University of Turin, University of Kent

Language: English

Topic: Bayesian Methods and Mixture Models

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W2082145367

FWCI: 3.709

Cites: 33

Cited by: 48

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