14 papers
Bayesian nonparametric inference for modal missing species and features
Alessandro Colombi, Mario Beraha, Daniele Durante +1
Species and feature sampling problems arise naturally whenever each observed unit is associated with one or more labels from a countable alphabet, and inference focuses on the unob…
Weighted persistence intensity regression
Matteo Pegoraro, Mario Beraha
Persistence diagrams summarize the multiscale topological structure of data, and in applications they often arrive paired with covariates. We develop nonparametric methodology and…
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…
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 partitions,…
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…
Hierarchical shot-noise Cox process mixtures for clustering across groups
Alessandro Carminati, Mario Beraha, Federico Camerlenghi +1
Clustering observations across partially exchangeable groups of data is a routine task in Bayesian nonparametrics. Previously proposed models allow for clustering across groups by…
Large-scale entity resolution via microclustering Ewens--Pitman random partitions
Mario Beraha, Stefano Favaro
We introduce the microclustering Ewens--Pitman model for random partitions, obtained by scaling the strength parameter of the Ewens--Pitman model linearly with the sample size. The…
Sufficient digits and density estimation: A Bayesian nonparametric approach using generalized finite Pólya trees
Mario Beraha, Jesper Møller
This paper proposes a novel approach for statistical modelling of a continuous random variable on , based on its digit representation . In general, …
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:…
Improved prediction of future user activity in online A/B testing
Lorenzo Masoero, Mario Beraha, Thomas Richardson +1
In online randomized experiments or A/B tests, accurate predictions of participant inclusion rates are of paramount importance. These predictions not only guide experimenters in op…
A Nonparametric Bayes Approach to Online Activity Prediction
Mario Beraha, Lorenzo Masoero, Stefano Favaro +1
Accurately predicting the onset of specific activities within defined timeframes holds significant importance in several applied contexts. In particular, accurate prediction of the…
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…
MCMC for Bayesian nonparametric mixture modeling under differential privacy
Mario Beraha, Stefano Favaro, Vinayak Rao
Estimating the probability density of a population while preserving the privacy of individuals in that population is an important and challenging problem that has received consider…