5 citations · 15 across the 16 of their papers we have counts for
18 papers
Similarity-Driven Proposals for MCMC Algorithms on Discrete Spaces
Luca Aiello, Raffaele Argiento, Alexandros Beskos +1
Recent research has led to the development of MCMC algorithms with likelihood-informed proposals when targeting posterior distributions supported on discrete state spaces. Our work…
Modeling Spatio-Temporal Dynamics of Obesity in Italian Regions Via Bayesian Beta Regression
Luciano Rota, Raffaele Argiento, Michela Cameletti
In this paper we investigate the spatio-temporal dynamics of obesity rates across Italian regions from 2010 to 2022, aiming to identify spatial and temporal trends and assess poten…
Addressing Phase Discrepancies in Functional Data: A Bayesian Approach for Accurate Alignment and Smoothing
Jacopo Gardella, Raffaele Argiento, Alessandro Casa +1
In many real-world applications, functional data exhibit considerable variability in both amplitude and phase. This is especially true in biomechanical data such as the knee flexio…
Bayesian nonparametric clustering for spatio-temporal data, with an application to air pollution
Luca Aiello, Raffaele Argiento, Sirio Legramanti +1
Air pollution is a major global health hazard, with fine particulate matter (PM10) linked to severe respiratory and cardiovascular diseases. Hence, analyzing and clustering spatio-…
Bayesian discovery of species in multiple areas
Alessandro Colombi, Raffaele Argiento, Federico Camerlenghi +1
In ecology, the description of species composition and biodiversity calls for statistical methods that involve estimating features of interest in unobserved samples based on an obs…
Hierarchical Mixture of Finite Mixtures
Alessandro Colombi, Raffaele Argiento, Federico Camerlenghi +1
Statistical modelling in the presence of data organized in groups is a crucial task in Bayesian statistics. The present paper conceives a mixture model based on a novel family of B…
Survival modelling of smartphone trigger data for earthquake parameter estimation in early warning. With applications to 2023 Turkish-Syrian and 2019 Ridgecrest events
Luca Aiello, Raffaele Argiento, Francesco Finazzi +1
Crowdsourced smartphone-based earthquake early warning systems recently emerged as reliable alternatives to the more expensive solutions based on scientific-grade instruments. For…
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…
Model-based clustering of categorical data based on the Hamming distance
Raffaele Argiento, Edoardo Filippi-Mazzola, Lucia Paci
A model-based approach is developed for clustering categorical data with no natural ordering. The proposed method exploits the Hamming distance to define a family of probability ma…
Clustering blood donors via mixtures of product partition models with covariates
Raffaele Argiento, Riccardo Corradin, Alessandra Guglielmi +1
Motivated by the problem of accurately predicting gap times between successive blood donations, we present here a general class of Bayesian nonparametric models for clustering. The…
Personalized Treatment Selection via Product Partition Models with Covariates
Matteo Pedone, Raffaele Argiento, Francesco C. Stingo
Precision medicine is an approach for disease treatment that defines treatment strategies based on the individual characteristics of the patients. Motivated by an open problem in c…
Learning block structured graphs in Gaussian graphical models
Alessandro Colombi, Raffaele Argiento, Lucia Paci +1
Within the framework of Gaussian graphical models, a prior distribution for the underlying graph is introduced to induce a block structure in the adjacency matrix of the graph and…
Gaussian graphical modeling for spectrometric data analysis
Laura Codazzi, Alessandro Colombi, Matteo Gianella +3
Motivated by the analysis of spectrometric data, we introduce a Gaussian graphical model for learning the dependence structure among frequency bands of the infrared absorbance spec…
Bayesian GARCH Modeling of Functional Sports Data
Patric Dolmeta, Raffaele Argiento, Silvia Montagna
The use of statistical methods in sport analytics has gained a rapidly growing interest over the last decade, and nowadays is common practice. In particular, the interest in unders…
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…
Bayesian isotonic logistic regression via constrained splines: an application to estimating the serve advantage in professional tennis
Silvia Montagna, Vanessa Orani, Raffaele Argiento
In professional tennis, it is often acknowledged that the server has an initial advantage. Indeed, the majority of points are won by the server, making the serve one of the most im…
Is infinity that far? A Bayesian nonparametric perspective of finite mixture models
Raffaele Argiento, Maria De Iorio
Mixture models are one of the most widely used statistical tools when dealing with data from heterogeneous populations. This paper considers the long-standing debate over finite mi…
A priori truncation method for posterior sampling from homogeneous normalized completely random measure mixture models
Raffaele Argiento, Ilaria Bianchini, Alessandra Guglielmi
This paper adopts a Bayesian nonparametric mixture model where the mixing distribution belongs to the wide class of normalized homogeneous completely random measures. We propose a…