Marco Scutari

Orcid: 0000-0002-2151-7266

According to our database1, Marco Scutari authored at least 32 papers between 2009 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Learning Bayesian networks with heterogeneous agronomic data sets via mixed-effect models and hierarchical clustering.
Eng. Appl. Artif. Intell., 2024

Entropy and the Kullback-Leibler Divergence for Bayesian Networks: Computational Complexity and Efficient Implementation.
Algorithms, 2024

2023
Risk Assessment of Lymph Node Metastases in Endometrial Cancer Patients: A Causal Approach.
CoRR, 2023

fairml: A Statistician's Take on Fair Machine Learning Modelling.
CoRR, 2023

Analyzing Complex Systems with Cascades Using Continuous-Time Bayesian Networks.
Proceedings of the 30th International Symposium on Temporal Representation and Reasoning, 2023

Causal Discovery with Missing Data in a Multicentric Clinical Study.
Proceedings of the Artificial Intelligence in Medicine, 2023

Towards a Transportable Causal Network Model Based on Observational Healthcare Data.
Proceedings of the 2nd AIxIA Workshop on Artificial Intelligence For Healthcare (HC@AIxIA 2023) co-located with the 22nd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2023), 2023

2022
Achieving fairness with a simple ridge penalty.
Stat. Comput., 2022

A Bayesian hierarchical score for structure learning from related data sets.
Int. J. Approx. Reason., 2022

Using Mixed-Effect Models to Learn Bayesian Networks from Related Data Sets.
CoRR, 2022

Comments on: "Hybrid Semiparametric Bayesian Networks".
CoRR, 2022

Bayesian network analysis reveals the interplay of intracranial aneurysm rupture risk factors.
Comput. Biol. Medicine, 2022

Using Mixed-Effects Models to Learn Bayesian Networks from Related Data Sets.
Proceedings of the International Conference on Probabilistic Graphical Models, 2022

Risk Assessment of Lymph Node Metastasis in Endometrial Cancer Patients: A Causal Approach.
Proceedings of the 1st AIxIA Workshop on Artificial Intelligence For Healthcare (HC@AIxIA 2022) co-located with the 21st International Conference of the Italian Association for Artificial Intelligence (AIxIA 2022), 2022

2021
A constraint-based algorithm for the structural learning of continuous-time Bayesian networks.
Int. J. Approx. Reason., 2021

Learning Bayesian networks from incomplete data with the node-average likelihood.
Int. J. Approx. Reason., 2021

Achieving Fairness with a Simple Ridge Penalty.
CoRR, 2021

2020
Constraint-Based Learning for Continuous-Time Bayesian Networks.
CoRR, 2020

Hard and Soft EM in Bayesian Network Learning from Incomplete Data.
Algorithms, 2020

Constraing-Based Learning for Continous-Time Bayesian Networks.
Proceedings of the International Conference on Probabilistic Graphical Models, 2020

Identifiability and Consistency of Bayesian Network Structure Learning from Incomplete Data.
Proceedings of the International Conference on Probabilistic Graphical Models, 2020

Structure Learning from Related Data Sets with a Hierarchical Bayesian Score.
Proceedings of the International Conference on Probabilistic Graphical Models, 2020

2019
Learning Bayesian networks from big data with greedy search: computational complexity and efficient implementation.
Stat. Comput., 2019

Who learns better Bayesian network structures: Accuracy and speed of structure learning algorithms.
Int. J. Approx. Reason., 2019

2018
Who Learns Better Bayesian Network Structures: Constraint-Based, Score-based or Hybrid Algorithms?
Proceedings of the International Conference on Probabilistic Graphical Models, 2018

2017
Dirichlet Bayesian Network Scores and the Maximum Entropy Principle.
Proceedings of the 3rd Workshop on Advanced Methodologies for Bayesian Networks, 2017

2016
An Empirical-Bayes Score for Discrete Bayesian Networks.
Proceedings of the Probabilistic Graphical Models - Eighth International Conference, 2016

2015
Graphical Modelling in Genetics and Systems Biology.
Proceedings of the Foundations of Biomedical Knowledge Representation, 2015

Personalised Medicine: Taking a New Look at the Patient.
Proceedings of the Foundations of Biomedical Knowledge Representation, 2015

2014
Bayesian Network Constraint-Based Structure Learning Algorithms: Parallel and Optimised Implementations in the bnlearn R Package.
CoRR, 2014

2013
Identifying significant edges in graphical models of molecular networks.
Artif. Intell. Medicine, 2013

2009
NATbox: a network analysis toolbox in R.
BMC Bioinform., 2009


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