162 citations · 334 across the 13 of their papers we have counts for
7 papers · 1 filter
Markov Stick-breaking Processes
María F. Gil-Leyva, Antonio Lijoi, Ramsés H. Mena +1
Stick-breaking has a long history and is one of the most popular procedures for constructing random discrete distributions in Statistics and Machine Learning. In particular, due to…
A Wasserstein index of dependence for random measures
Marta Catalano, Hugo Lavenant, Antonio Lijoi +1
Optimal transport and Wasserstein distances are flourishing in many scientific fields as a means for comparing and connecting random structures. Here we pioneer the use of an optim…
Inner spike and slab Bayesian nonparametric models
Antonio Canale, Antonio Lijoi, Bernardo Nipoti +1
Discrete Bayesian nonparametric models whose expectation is a convex linear combination of a point mass at some point of the support and a diffuse probability distribution allow to…
Asymptotic behavior of the number of distinct values in a sample from the geometric stick-breaking process
Pierpaolo De Blasi, Ramsés H. Mena, Igor Prünster
Discrete random probability measures are a key ingredient of Bayesian nonparametric inferential procedures. A sample generates ties with positive probability and a fundamental obje…
Latent nested nonparametric priors
Federico Camerlenghi, David B. Dunson, Antonio Lijoi +2
Discrete random structures are important tools in Bayesian nonparametrics and the resulting models have proven effective in density estimation, clustering, topic modeling and predi…
Are Gibbs-type priors the most natural generalization of the Dirichlet process?
P. De Blasi, S. Favaro, A. Lijoi +3
Discrete random probability measures and the exchangeable random partitions they induce are key tools for addressing a variety of estimation and prediction problems in Bayesian inf…
Bayesian inference with dependent normalized completely random measures
Antonio Lijoi, Bernardo Nipoti, Igor Prünster
The proposal and study of dependent prior processes has been a major research focus in the recent Bayesian nonparametric literature. In this paper, we introduce a flexible class of…