On functional central limit theorems of Bayesian nonparametric priors

Luai Al Labadi () and Ibrahim Abdelrazeq ()
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Luai Al Labadi: University of Toronto
Ibrahim Abdelrazeq: Rhodes College

Statistical Methods & Applications, 2017, vol. 26, issue 2, No 2, 215-229

Abstract: Abstract A general approach to derive the weak convergence, when centered and rescaled, of certain Bayesian nonparametric priors is proposed. This method may be applied to a wide range of processes including, for instance, nondecreasing nonnegative pure jump Lévy processes and normalized nondecreasing nonnegative pure jump Lévy processes with known finite dimensional distributions. Examples clarifying this approach involve the beta process in latent feature models and the Dirichlet process.

Keywords: Beta process; Dirichlet process; Lévy processes; Nonparametric Bayesian inference; Processes with independent increments; Quantile process; Weak convergence; Primary 62F15; Secondary 60F05 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10260-016-0365-8

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