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Activity Number: 611 - Nonparametric Priors for Exchangeable Data and Beyond
Type: Topic Contributed
Date/Time: Thursday, August 2, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #328633 Presentation
Title: Epsilon-Approximations to the Pitman-Yor Process
Author(s): Pierpaolo De Blasi*
Companies: University of Turin
Keywords: approximation; asymptotic distribution; Bayesian nonparametric inference; Pitman-Yor process; random functionals; stopping rule

In this paper we consider approximations to the Pitman-Yor process obtained by truncating the stick-breaking representation. The truncation is determined by a random stopping rule that achieves an almost sure control on the approximation error in total variation distance. We derive the asymptotic distribution of the random truncation point as the approximation error epsilon goes to zero in terms of a polynomially tilted positive stable distribution. The usefulness of this theoretical result is demonstrated by devising a sampling algorithm to approximate functionals of the epsilon-version of the Pitman-Yor process.

Authors who are presenting talks have a * after their name.

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