Maria De Iorio (University College London)

Dependent Generalised Dirichlet Process Priors

We propose a novel Bayesian nonparametric process prior for modelling a collections of random discrete distributions. This process is defined by combining a Generalised Dirichlet Process with a suitable Beta regression framework that introduces dependence among the discrete random distributions. This strategy allows for covariate dependent clustering of the observations. Some advantages of the proposed approach include wide applicability, ease of interpretation and efficient MCMC algorithms. The methodology is illustrated through two real data applications involving acute lymphoblastic leukaemia and London primary schools quality evaluations.