17 citations · 27 across the 14 of their papers we have counts for
17 papers
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…
Bayesian Mixtures Models with Repulsive and Attractive Atoms
Mario Beraha, Raffaele Argiento, Federico Camerlenghi +1
The study of almost surely discrete random probability measures is an active line of research in Bayesian nonparametrics. The idea of assuming interaction across the atoms of the r…
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…
Wasserstein posterior contraction rates in non-dominated Bayesian nonparametric models
Federico Camerlenghi, Emanuele Dolera, Stefano Favaro +1
Posterior contractions rates (PCRs) strengthen the notion of Bayesian consistency, quantifying the speed at which the posterior distribution concentrates on arbitrarily small neigh…
On Johnson's "sufficientness" postulates for features-sampling models
Federico Camerlenghi, Stefano Favaro
In the 1920's, the English philosopher W.E. Johnson introduced a characterization of the symmetric Dirichlet prior distribution in terms of its predictive distribution. This is typ…
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…
Nonparametric Bayesian multi-armed bandits for single cell experiment design
Federico Camerlenghi, Bianca Dumitrascu, Federico Ferrari +2
The problem of maximizing cell type discovery under budget constraints is a fundamental challenge for the collection and analysis of single-cell RNA-sequencing (scRNA-seq) data. In…
Consistent estimation of the missing mass for feature models
Fadhel Ayed, Marco Battiston, Federico Camerlenghi +1
Feature models are popular in machine learning and they have been recently used to solve many unsupervised learning problems. In these models every observation is endowed with a fi…
Optimal disclosure risk assessment
Federico Camerlenghi, Stefano Favaro, Zacharie Naulet +1
Protection against disclosure is a legal and ethical obligation for agencies releasing microdata files for public use. Consider a microdata sample of size from a finite populat…
A Good-Turing estimator for feature allocation models
Fadhel Ayed, Marco Battiston, Federico Camerlenghi +1
Feature allocation models generalize species sampling models by allowing every observation to belong to more than one species, now called features. Under the popular Bernoulli prod…
On consistent estimation of the missing mass
Fadhel Ayed, Marco Battiston, Federico Camerlenghi +1
Given samples from a population of individuals belonging to different types with unknown proportions, how do we estimate the probability of discovering a new type at the $(n+1)…
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…