Beta-Binomial stick-breaking non-parametric prior

Autor:	Gil-Leyva, María F., Mena, Ramsés H., Nicoleris, Theodoros
Rok vydání:	2019
Předmět:	Mathematics - Statistics Theory Mathematics - Probability Statistics - Methodology
Zdroj:	Electron. J. Statist., Volume 14, Number 1 (2020), 1479-1507
Druh dokumentu:	Working Paper
DOI:	10.1214/20-EJS1694
Popis:	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. Comment: 26 pages, 10 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1908.06602 Zobrazit plný text záznamu View this record from Arxiv