100 citations · 207 across the 28 of their papers we have counts for
12 papers · 1 filter
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…
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…
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…
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…
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…
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…
On Identifying Significant Edges in Graphical Models of Molecular Networks
Marco Scutari, Radhakrishnan Nagarajan
Objective: Modelling the associations from high-throughput experimental molecular data has provided unprecedented insights into biological pathways and signalling mechanisms. Graph…
Bayesian Network Structure Learning with Permutation Tests
Marco Scutari, Adriana Brogini
In literature there are several studies on the performance of Bayesian network structure learning algorithms. The focus of these studies is almost always the heuristics the learnin…
Introduction to Graphical Modelling
Marco Scutari, Korbinian Strimmer
The aim of this chapter is twofold. In the first part we will provide a brief overview of the mathematical and statistical foundations of graphical models, along with their fundame…
Learning Bayesian Networks with the bnlearn R Package
Marco Scutari
bnlearn is an R package which includes several algorithms for learning the structure of Bayesian networks with either discrete or continuous variables. Both constraint-based and sc…