Stick-Breaking Processes With Exchangeable Length Variables

María F. Gil–Leyva and Ramsés H. Mena

Journal of the American Statistical Association, 2023, vol. 118, issue 541, 537-550

Abstract: Our object of study is the general class of stick-breaking processes with exchangeable length variables. These generalize well-known Bayesian nonparametric 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 subclass we explain how, by tuning a single [0,1]-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.

Date: 2023
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DOI: 10.1080/01621459.2021.1941054

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