Finite mixture model for multiple sample data

In this paper, we propose a Bayesian nonparametric level-dependent mixture model\r\nfor clustering. To achieve this, we employ a vector of species sampling models with shared\r\natoms and level-specific weights. This results in multiple random probability measures with\r\na common support, which we use to perform both inter-level and within-level clustering of\r\nthe data. This approach enables us to take into account both heterogeneity and common\r\npatterns shared across levels in our clustering analysis. Specifically, we study the properties\r\nof the group-dependent clustering structure induced by our hierarchical mixture model. We\r\ndevelop both a marginal and a conditional Gibbs sampler to perform Bayesian inference.\r\nWe evaluate the model’s ability to recover the original clustering of the data and assess its\r\ngoodness of fit through simulated data.