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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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Geometric weight priors and their applications

Geometric weight priors and their applications

Chapter:
(p.271) 14 Geometric weight priors and their applications
Source:
Bayesian Theory and Applications
Author(s):

Ramsés H. Mena

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199695607.003.0014

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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