# Beta-Binomial stick-breaking non-parametric prior

arXiv: https://arxiv.org/abs/1908.06602 · HTML: https://mlsanity.com/paper/1908.06602/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2019.

## Built on by (most cited first, 1 of 1)

- [Entropy regularization in probabilistic clustering](https://mlsanity.com/paper/2307.10065/) (2023)

## Abstract (arXiv)

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.