9 papers
Bayesian Nonparametric Privacy-Preserving Synthetic Data Generation: I. Discrete Data
Maria Chiara Menicucci, Mario Beraha, Stefano Favaro +1
Synthetic data generation is a powerful approach to privacy-preserving statistical analysis, where data-release mechanisms are governed by a privacy-utility tradeoff: they should p…
Bayesian Mixture Models for Histograms: with Applications to Large Datasets
Richard L. Warr, Fernando A. Quintana, Alessandra Guglielmi +1
In many real-world scenarios, especially those involving privacy constraints or data summarization, data are available only in aggregated forms, such as histograms or frequency tab…
Online activity prediction via generalized Indian buffet process models
Mario Beraha, Lorenzo Masoero, Stefano Favaro +1
Online A/B tests are the standard tool for data-driven decision-making at scale. Among the design choices with the largest impact on statistical power is the triggering mechanism:…
Asymptotic regimes for maximum likelihood estimation in the Ewens--Pitman model: When the strength parameter matters
Filippo Ascolani, Mario Beraha, Stefano Favaro
We study the large sample asymptotic behaviour of the Maximum Likelihood Estimator of the discount and strength parameters in the Ewens--Pitman model for random partition…
Bayesian nonparametric boundary detection for multiple areal data
Matteo Gianella, Mario Beraha, Alessandra Guglielmi
We consider the problem of boundary detection for areal data, focusing on situations where for each areal unit multiple observations are available. We propose a Bayesian nonparamet…
Confidence intervals for maximum unseen probabilities, with application to sequential sampling design
Alessandro Colombi, Mario Beraha, Amichai Painsky +1
Discovery problems often require deciding whether additional sampling is needed to detect all categories whose prevalence exceeds a prespecified threshold. We study this question u…