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Bayesian nonparametric inference for modal missing species and features
Alessandro Colombi, Mario Beraha, Daniele Durante +1
Species and feature sampling problems arise naturally whenever each observed unit is associated with one or more labels from a countable alphabet, and inference focuses on the unob…
Bayesian conjugacy in probit, tobit, multinomial probit and extensions: A review and new results
Niccolò Anceschi, Augusto Fasano, Daniele Durante +1
A broad class of models that routinely appear in several fields can be expressed as partially or fully discretized Gaussian linear regressions. Besides including basic Gaussian res…
Concentration of discrepancy-based approximate Bayesian computation via Rademacher complexity
Sirio Legramanti, Daniele Durante, Pierre Alquier
There has been increasing interest on summary-free solutions for approximate Bayesian computation (ABC) which replace distances among summaries with discrepancies between the empir…
Bayesian Testing for Exogenous Partition Structures in Stochastic Block Models
Sirio Legramanti, Tommaso Rigon, Daniele Durante
Network data often exhibit block structures characterized by clusters of nodes with similar patterns of edge formation. When such relational data are complemented by additional inf…
Extended Stochastic Block Models with Application to Criminal Networks
Sirio Legramanti, Tommaso Rigon, Daniele Durante +1
Reliably learning group structures among nodes in network data is challenging in several applications. We are particularly motivated by studying covert networks that encode relatio…
A Class of Conjugate Priors for Multinomial Probit Models which Includes the Multivariate Normal One
Augusto Fasano, Daniele Durante
Multinomial probit models are routinely-implemented representations for learning how the class probabilities of categorical response data change with p observed predictors. Althoug…