3 citations · 3 across the 4 of their papers we have counts for
6 papers
Borrowing Space, Clustering Temporal Dynamics: Bayesian Regime Mapping and Spatial Prediction for European PM10
Luca Aiello, Lucia Clarotto
Daily particulate matter measurements from European monitoring networks vary both through spatially shared fluctuations and through differences in local temporal dynamics. Characte…
Amortized Bayesian Disease Mapping and Boundary Detection on Heterogeneous Spatial Graphs
Luca Aiello, Sudipto Banerjee
Spatial disease maps help public-health researchers identify geographic inequalities, but standard Bayesian smoothing can obscure localized disparities when neighboring communities…
Spatial Disease Mapping and Disparity Detection Using Generative AI: An Amortized Bayesian Learning Framework
Luca Aiello, Sudipto Banerjee
We introduce an amortized Bayesian framework for spatial boundary detection that generalizes posterior inference across areal graphs with varying numbers of regions and diverse adj…
Similarity-Driven Proposals for MCMC Algorithms on Discrete Spaces
Luca Aiello, Raffaele Argiento, Alexandros Beskos +1
Recent research has led to the development of MCMC algorithms with likelihood-informed proposals when targeting posterior distributions supported on discrete state spaces. Our work…
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-…
Detecting Spatial Health Disparities Using Disease Maps
Luca Aiello, Sudipto Banerjee
Epidemiologists commonly use regional aggregates of health outcomes to map mortality or incidence rates and identify geographic disparities. However, to detect health disparities a…