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We propose the Plaid Atoms Model (PAM), a novel Bayesian nonparametric modelfor grouped data. Founded on an idea of `atom skipping', PAM is part of awell-established category of models that generate dependent randomdistributions and clusters across multiple groups. Atom skipping referrs tostochastically assigning 0 weights to atoms in an infinite mixture. Deployingatom skipping across groups, PAM produces a dependent clustering pattern withoverlapping and non-overlapping clusters across groups. As a result,interpretable posterior inference is possible such as reporting the posteriorprobability of a cluster being exclusive to a single group or shared among asubset of groups. We discuss the theoretical properties of the proposed andrelated models. Minor extensions of the proposed model for multivariate orcount data are presented. Simulation studies and applications using real-worlddatasets illustrate the performance of the new models with comparison toexisting models.

A Class of Dependent Random Distributions Based on Atom Skipping