10 citations · 21 across the 18 of their papers we have counts for
20 papers
Repulsive Mixture Model with Projection Determinantal Point Process
Ziyi Song, Federico Camerlenghi, Weining Shen +2
In many scientific domains, clustering aims to reveal interpretable latent structure that reflects relevant subpopulations or processes. Widely used Bayesian mixture models for mod…
Palm distributions of superposed point processes for statistical inference
Mario Beraha, Federico Camerlenghi, Lorenzo Ghilotti
Palm distributions play a central role in the study of point processes and their associated summary statistics. In this paper, we characterize the Palm distributions of the superpo…
Extended feature allocation models
Mario Beraha, Federico Camerlenghi, Lorenzo Ghilotti
Feature allocation models are Bayesian nonparametric tools tailored to data in which each observation can simultaneously exhibit multiple characteristics, or features. A fundamenta…
On the Palm distribution of superposition of point processes
Mario Beraha, Federico Camerlenghi
Palm distributions are critical in the study of point processes. In the present paper we focus on a point process defined as the superposition, i.e., sum, of two independent po…
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…
Normalized Latent Measure Factor Models
Mario Beraha, Jim E. Griffin
We propose a methodology for modeling and comparing probability distributions within a Bayesian nonparametric framework. Building on dependent normalized random measures, we consid…
BayesMix: Bayesian Mixture Models in C++
Mario Beraha, Bruno Guindani, Matteo Gianella +1
We describe BayesMix, a C++ library for MCMC posterior simulation for general Bayesian mixture models. The goal of BayesMix is to provide a self-contained ecosystem to perform infe…
Bayesian Nonparametric Vector Autoregressive Models via a Logit Stick-breaking Prior: an Application to Child Obesity
Mario Beraha, Alessandra Guglielmi, Fernando A. Quintana +3
Overweight and obesity in adults are known to be associated with risks of metabolic and cardiovascular diseases. Because obesity is an epidemic, increasingly affecting children, it…
Bayesian nonparametric model based clustering with intractable distributions: an ABC approach
Mario Beraha, Riccardo Corradin
Bayesian nonparametric mixture models offer a rich framework for model based clustering. We consider the situation where the kernel of the mixture is available only up to an intrac…
JAGS, NIMBLE, Stan: a detailed comparison among Bayesian MCMC software
Mario Beraha, Daniele Falco, Alessandra Guglielmi
The aim of this work is the comparison of the performance of the three popular software platforms JAGS, NIMBLE and Stan. These probabilistic programming languages are able to autom…
Projected Statistical Methods for Distributional Data on the Real Line with the Wasserstein Metric
Matteo Pegoraro, Mario Beraha
We present a novel class of projected methods, to perform statistical analysis on a data set of probability distributions on the real line, with the 2-Wasserstein metric. We focus…
MCMC computations for Bayesian mixture models using repulsive point processes
Mario Beraha, Raffaele Argiento, Jesper Møller +1
Repulsive mixture models have recently gained popularity for Bayesian cluster detection. Compared to more traditional mixture models, repulsive mixture models produce a smaller num…
Spatially dependent mixture models via the Logistic Multivariate CAR prior
Mario Beraha, Matteo Pegoraro, Riccardo Peli +1
We consider the problem of spatially dependent areal data, where for each area independent observations are available, and propose to model the density of each area through a finit…
The semi-hierarchical Dirichlet Process and its application to clustering homogeneous distributions
Mario Beraha, Alessandra Guglielmi, Fernando A. Quintana
Assessing homogeneity of distributions is an old problem that has received considerable attention, especially in the nonparametric Bayesian literature. To this effect, we propose t…
Feature Selection via Mutual Information: New Theoretical Insights
Mario Beraha, Alberto Maria Metelli, Matteo Papini +2
Mutual information has been successfully adopted in filter feature-selection methods to assess both the relevancy of a subset of features in predicting the target variable and the…