100 citations · 207 across the 28 of their papers we have counts for
34 papers
Benchmarking Constraint-Based Bayesian Structure Learning Algorithms: Role of Network Topology
Radha Nagarajan, Marco Scutari
Modeling the associations between real world entities from their multivariate cross-sectional profiles can provide cues into the concerted working of these entities as a system. Se…
Towards a Transportable Causal Network Model Based on Observational Healthcare Data
Alice Bernasconi, Alessio Zanga, Peter J. F. Lucas +2
Over the last decades, many prognostic models based on artificial intelligence techniques have been used to provide detailed predictions in healthcare. Unfortunately, the real-worl…
Analyzing Complex Systems with Cascades Using Continuous-Time Bayesian Networks
Alessandro Bregoli, Karin Rathsman, Marco Scutari +2
Interacting systems of events may exhibit cascading behavior where events tend to be temporally clustered. While the cascades themselves may be obvious from the data, it is importa…
Learning Bayesian Networks with Heterogeneous Agronomic Data Sets via Mixed-Effect Models and Hierarchical Clustering
Lorenzo Valleggi, Marco Scutari, Federico Mattia Stefanini
Maize, a crucial crop globally cultivated across vast regions, especially in sub-Saharan Africa, Asia, and Latin America, occupies 197 million hectares as of 2021. Various statisti…
Risk Assessment of Lymph Node Metastases in Endometrial Cancer Patients: A Causal Approach
Alessio Zanga, Alice Bernasconi, Peter J. F. Lucas +4
Assessing the pre-operative risk of lymph node metastases in endometrial cancer patients is a complex and challenging task. In principle, machine learning and deep learning models…
fairml: A Statistician's Take on Fair Machine Learning Modelling
Marco Scutari
The adoption of machine learning in applications where it is crucial to ensure fairness and accountability has led to a large number of model proposals in the literature, largely f…
The Impact of Missing Data on Causal Discovery: A Multicentric Clinical Study
Alessio Zanga, Alice Bernasconi, Peter J. F. Lucas +4
Causal inference for testing clinical hypotheses from observational data presents many difficulties because the underlying data-generating model and the associated causal graph are…
Using Mixed-Effects Models to Learn Bayesian Networks from Related Data Sets
Marco Scutari, Christopher Marquis, Laura Azzimonti
We commonly assume that data are a homogeneous set of observations when learning the structure of Bayesian networks. However, they often comprise different data sets that are relat…
Comments on: "Hybrid Semiparametric Bayesian Networks"
Marco Scutari
Invited discussion on the paper "Hybrid Semiparametric Bayesian Networks" by David Atienza, Pedro Larranaga and Concha Bielza (TEST, 2022).
Achieving Fairness with a Simple Ridge Penalty
Marco Scutari, Francesca Panero, Manuel Proissl
In this paper we present a general framework for estimating regression models subject to a user-defined level of fairness. We enforce fairness as a model selection step in which we…
Hard and Soft EM in Bayesian Network Learning from Incomplete Data
Andrea Ruggieri, Francesco Stranieri, Fabio Stella +1
Incomplete data are a common feature in many domains, from clinical trials to industrial applications. Bayesian networks (BNs) are often used in these domains because of their grap…
A Bayesian Hierarchical Score for Structure Learning from Related Data Sets
Laura Azzimonti, Giorgio Corani, Marco Scutari
Score functions for learning the structure of Bayesian networks in the literature assume that data are a homogeneous set of observations; whereas it is often the case that they com…
A Constraint-Based Algorithm for the Structural Learning of Continuous-Time Bayesian Networks
Alessandro Bregoli, Marco Scutari, Fabio Stella
Dynamic Bayesian networks have been well explored in the literature as discrete-time models: however, their continuous-time extensions have seen comparatively little attention. In…
Learning Bayesian Networks from Incomplete Data with the Node-Average Likelihood
Tjebbe Bodewes, Marco Scutari
Bayesian network (BN) structure learning from complete data has been extensively studied in the literature. However, fewer theoretical results are available for incomplete data, an…
Bayesian Network Models for Incomplete and Dynamic Data
Marco Scutari
Bayesian networks are a versatile and powerful tool to model complex phenomena and the interplay of their components in a probabilistically principled way. Moving beyond the compar…
Who Learns Better Bayesian Network Structures: Accuracy and Speed of Structure Learning Algorithms
Marco Scutari, Catharina Elisabeth Graafland, José Manuel Gutiérrez
Three classes of algorithms to learn the structure of Bayesian networks from data are common in the literature: constraint-based algorithms, which use conditional independence test…
Learning Bayesian Networks from Big Data with Greedy Search: Computational Complexity and Efficient Implementation
Marco Scutari, Claudia Vitolo, Allan Tucker
Learning the structure of Bayesian networks from data is known to be a computationally challenging, NP-hard problem. The literature has long investigated how to perform structure l…
Dirichlet Bayesian Network Scores and the Maximum Relative Entropy Principle
Marco Scutari
A classic approach for learning Bayesian networks from data is to identify a maximum a posteriori (MAP) network structure. In the case of discrete Bayesian networks, MAP networks a…
Beyond Uniform Priors in Bayesian Network Structure Learning
Marco Scutari
Bayesian network structure learning is often performed in a Bayesian setting, evaluating candidate structures using their posterior probabilities for a given data set. Score-based…
Bayesian Networks Analysis of Malocclusion Data
Marco Scutari, Pietro Auconi, Guido Caldarelli +1
In this paper we use Bayesian networks to determine and visualise the interactions among various Class III malocclusion maxillofacial features during growth and treatment. We start…
An Empirical-Bayes Score for Discrete Bayesian Networks
Marco Scutari
Bayesian network structure learning is often performed in a Bayesian setting, by evaluating candidate structures using their posterior probabilities for a given data set. Score-bas…
Using Genetic Distance to Infer the Accuracy of Genomic Prediction
Marco Scutari, Ian Mackay, David Balding
The prediction of phenotypic traits using high-density genomic data has many applications such as the selection of plants and animals of commercial interest; and it is expected to…
Bayesian Network Constraint-Based Structure Learning Algorithms: Parallel and Optimised Implementations in the bnlearn R Package
Marco Scutari
It is well known in the literature that the problem of learning the structure of Bayesian networks is very hard to tackle: its computational complexity is super-exponential in the…
Multiple Quantitative Trait Analysis Using Bayesian Networks
Marco Scutari, Phil Howell, David J. Balding +1
Models for genome-wide prediction and association studies usually target a single phenotypic trait. However, in animal and plant genetics it is common to record information on mult…