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20122026
most citedAre Gibbs-type priors the most natural generalization of the Dirichlet process?

162 citations · 334 across the 13 of their papers we have counts for

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5 papers · 1 filter

stat.ME2026

Principled Estimation and Prediction with Competing Risks: a Bayesian Nonparametric Approach

Claudio Del Sole, Antonio Lijoi, Igor Prünster

Competing risks occur in survival analysis when multiple causes of death are present. They play a prominent role in several domains extending beyond biostatistics to encompass epid…

stat.ME2024

Partially exchangeable stochastic block models for (node-colored) multilayer networks

Daniele Durante, Francesco Gaffi, Antonio Lijoi +1

Multilayer networks generalize single-layered connectivity data in several directions. These generalizations include, among others, settings where multiple types of edges are obser…

stat.ME2022★ 3 cited

Model Selection for Maternal Hypertensive Disorders with Symmetric Hierarchical Dirichlet Processes

Beatrice Franzolini, Antonio Lijoi, Igor Prünster

Hypertensive disorders of pregnancy occur in about 10% of pregnant women around the world. Though there is evidence that hypertension impacts maternal cardiac functions, the relati…

stat.ME2022★ 22 cited

Flexible clustering via hidden hierarchical Dirichlet priors

Antonio Lijoi, Igor Prünster, Giovanni Rebaudo

The Bayesian approach to inference stands out for naturally allowing borrowing information across heterogeneous populations, with different samples possibly sharing the same distri…

stat.ME2013★ 58 cited

Modeling with Normalized Random Measure Mixture Models

Ernesto Barrios, Antonio Lijoi, Luis E. Nieto-Barajas +1

The Dirichlet process mixture model and more general mixtures based on discrete random probability measures have been shown to be flexible and accurate models for density estimatio…