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Bayesian nonparametric clustering for spatio-temporal data, with an application to air pollution
Luca Aiello, Raffaele Argiento, Sirio Legramanti +1
Air pollution is a major global health hazard, with fine particulate matter (PM10) linked to severe respiratory and cardiovascular diseases. Hence, analyzing and clustering spatio-…
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…
Bayesian cumulative shrinkage for infinite factorizations
Sirio Legramanti, Daniele Durante, David B. Dunson
There is a wide variety of models in which the dimension of the parameter space is unknown. For example, in factor analysis the number of latent factors is typically not known and…