Title: Bayesian analysis of (extended) feature allocation models: predictions, sufficientness, and applications
Abstract: We introduce the class of extended feature allocation models, which generalizes standard feature allocations by allowing dependence across features' labels. In particular, we study a Bayesian nonparametric model where data are conditionally i.i.d. from a Bernoulli product model, and the random parameter of interest is an almost surely discrete random measure seen as a functional of a point process. General expressions for the marginal, posterior, and predictive laws are obtained and exploited to derive two sufficientness postulates. Given a sample of size n, we characte...