17 citations · 27 across the 14 of their papers we have counts for
8 papers · 1 filter
Nested Compound Random Measures
Federico Camerlenghi, Riccardo Corradin, Andrea Ongaro
Nested nonparametric processes are vectors of random probability measures widely used in the Bayesian literature to model the dependence across distinct, though related, groups of…
Hierarchical Mixture of Finite Mixtures
Alessandro Colombi, Raffaele Argiento, Federico Camerlenghi +1
Statistical modelling in the presence of data organized in groups is a crucial task in Bayesian statistics. The present paper conceives a mixture model based on a novel family of B…
A Bayesian Nonparametric Approach to Species Sampling Problems with Ordering
Cecilia Balocchi, Federico Camerlenghi, Stefano Favaro
Species-sampling problems (SSPs) refer to a vast class of statistical problems calling for the estimation of (discrete) functionals of the unknown species composition of an unobser…
Contaminated Gibbs-type priors
Federico Camerlenghi, Riccardo Corradin, Andrea Ongaro
Gibbs-type priors are widely used as key components in several Bayesian nonparametric models. By virtue of their flexibility and mathematical tractability, they turn out to be pred…
Scaled process priors for Bayesian nonparametric estimation of the unseen genetic variation
Federico Camerlenghi, Stefano Favaro, Lorenzo Masoero +1
There is a growing interest in the estimation of the number of unseen features, mostly driven by biological applications. A recent work brought out a peculiar property of the popul…
An Information Theoretic approach to Post Randomization Methods under Differential Privacy
Fadhel Ayed, Marco Battiston, Federico Camerlenghi
Post Randomization Methods (PRAM) are among the most popular disclosure limitation techniques for both categorical and continuous data. In the categorical case, given a stochastic…
A Common Atom Model for the Bayesian Nonparametric Analysis of Nested Data
Francesco Denti, Federico Camerlenghi, Michele Guindani +1
The use of high-dimensional data for targeted therapeutic interventions requires new ways to characterize the heterogeneity observed across subgroups of a specific population. In p…
More for less: Predicting and maximizing genetic variant discovery via Bayesian nonparametrics
Lorenzo Masoero, Federico Camerlenghi, Stefano Favaro +1
While the cost of sequencing genomes has decreased dramatically in recent years, this expense often remains non-trivial. Under a fixed budget, then, scientists face a natural trade…