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2020 Beta-Binomial stick-breaking non-parametric prior
María F. Gil–Leyva, Ramsés H. Mena, Theodoros Nicoleris
Electron. J. Statist. 14(1): 1479-1507 (2020). DOI: 10.1214/20-EJS1694


A new class of nonparametric prior distributions, termed Beta-Binomial stick-breaking process, is proposed. By allowing the underlying length random variables to be dependent through a Beta marginals Markov chain, an appealing discrete random probability measure arises. The chain’s dependence parameter controls the ordering of the stick-breaking weights, and thus tunes the model’s label-switching ability. Also, by tuning this parameter, the resulting class contains the Dirichlet process and the Geometric process priors as particular cases, which is of interest for MCMC implementations.

Some properties of the model are discussed and a density estimation algorithm is proposed and tested with simulated datasets.


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María F. Gil–Leyva. Ramsés H. Mena. Theodoros Nicoleris. "Beta-Binomial stick-breaking non-parametric prior." Electron. J. Statist. 14 (1) 1479 - 1507, 2020.


Received: 1 November 2019; Published: 2020
First available in Project Euclid: 9 April 2020

zbMATH: 07200235
MathSciNet: MR4082475
Digital Object Identifier: 10.1214/20-EJS1694

Keywords: Beta-Binomial Markov chain , Density estimation , Dirichlet process prior , geometric process prior , stick-breaking prior


Vol.14 • No. 1 • 2020
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