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20122022
most citedAre Gibbs-type priors the most natural generalization of the Dirichlet process?

162 citations · 214 across the 13 of their papers we have counts for

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10 papers · 1 filter

math.ST20222 cited

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…

math.ST2021

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…

math.ST2019

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…

math.ST2019

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…

math.ST2019

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…

math.ST2018

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)…