Stick-breaking processes with exchangeable length variables
Abstract
Our object of study is the general class of stick-breaking processes with exchangeable length variables. These generalize well-known Bayesian non-parametric priors in an unexplored direction. We give conditions to assure the respective species sampling process is proper and the corresponding prior has full support. For a rich sub-class we explain how, by tuning a single -valued parameter, the stochastic ordering of the weights can be modulated, and Dirichlet and Geometric priors can be recovered. A general formula for the distribution of the latent allocation variables is derived and an MCMC algorithm is proposed for density estimation purposes.
Cite
@article{arxiv.2008.04475,
title = {Stick-breaking processes with exchangeable length variables},
author = {María F. Gil-Leyva and Ramsés H. Mena},
journal= {arXiv preprint arXiv:2008.04475},
year = {2021}
}
Comments
Accepted for publication by the Journal of the American Statistical Association. 44 pages, 11 figures, supplementary material