Beta-Binomial stick-breaking non-parametric prior

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 p...

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Bibliographic Details
Published inarXiv.org
Main Authors Gil-Leyva, María F, Mena, Ramsés H, Nicoleris, Theodoros
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 11.08.2020
Subjects
Algorithms
Breaking
Computer simulation
Dependence
Dependent variables
Dirichlet problem
Markov analysis
Markov chains
Mathematical models
Mathematics - Probability
Mathematics - Statistics Theory
Parameters
Random variables
Statistics - Methodology
Statistics - Theory
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Summary: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 fast convergence of MCMC implementations. Some properties of the model are discussed and a density estimation algorithm is proposed and tested with simulated datasets.
ISSN:2331-8422
DOI:10.48550/arxiv.1908.06602