Latent Nested Nonparametric Priors (with Discussion).

ABSTRACT: Discrete random structures are important tools in Bayesian nonparametrics and the resulting models have proven effective in density estimation, clustering, topic modeling and prediction, among others. In this paper, we consider nested processes and study the dependence structures they induce. Dependence ranges between homogeneity, corresponding to full exchangeability, and maximum heterogeneity, corresponding to (unconditional) independence across samples. The popular nested Dirichlet process is shown to degenerate to the fully exchangeable case when there are ties across samples at the observed or latent level. To overcome this drawback, inherent to nesting general discrete random measures, we introduce a novel class of latent nested processes. These are obtained by adding common and grou

SUBMITTER: Camerlenghi F 

PROVIDER: S-EPMC9381042 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature