10 citations · 21 across the 18 of their papers we have counts for
6 papers · 1 filter
A smoothed-Bayesian approach to frequency recovery from sketched data
Mario Beraha, Stefano Favaro, Matteo Sesia
We provide a novel statistical perspective on a classical problem at the intersection of computer science and information theory: recovering the empirical frequency of a symbol in…
Wasserstein Principal Component Analysis for Circular Measures
Mario Beraha, Matteo Pegoraro
We consider the 2-Wasserstein space of probability measures supported on the unit-circle, and propose a framework for Principal Component Analysis (PCA) for data living in such a s…
Transform-scaled process priors for trait allocations in Bayesian nonparametrics
Mario Beraha, Stefano Favaro
Completely random measures (CRMs) provide a broad class of priors, arguably, the most popular, for Bayesian nonparametric (BNP) analysis of trait allocations. As a peculiar propert…
Random measure priors in Bayesian recovery from sketches
Mario Beraha, Stefano Favaro, Matteo Sesia
This paper introduces a Bayesian nonparametric approach to frequency recovery from lossy-compressed discrete data, leveraging all information contained in a sketch obtained through…
Bayesian clustering of high-dimensional data via latent repulsive mixtures
Lorenzo Ghilotti, Mario Beraha, Alessandra Guglielmi
Model-based clustering of moderate or large dimensional data is notoriously difficult. We propose a model for simultaneous dimensionality reduction and clustering by assuming a mix…
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…