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Bayesian Theory and Applications$
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Paul Damien, Petros Dellaportas, Nicholas G. Polson, and David A. Stephens

Print publication date: 2013

Print ISBN-13: 9780199695607

Published to Oxford Scholarship Online: May 2013

DOI: 10.1093/acprof:oso/9780199695607.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 13 May 2021

Geometric weight priors and their applications

Geometric weight priors and their applications

(p.271) 14 Geometric weight priors and their applications
Bayesian Theory and Applications

Ramsés H. Mena

Oxford University Press

This chapter discusses random probability measures (r.p.m.s) that result in robust choice of nonparametric priors due to their simpler weight structure. The key idea is that having simpler weights results in a more efficient use of the infinite collection of locations to assign the required mass to a particular set B ∈ Χ. Having simpler weights also results in easier ways to estimate models and extend them to non-exchangeable contexts.

Keywords:   random probability measures, nonparametric prior, geometric weights

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