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Marco Scutari
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2020 – today
- 2024
- [j12]Marco Scutari
:
Entropy and the Kullback-Leibler Divergence for Bayesian Networks: Computational Complexity and Efficient Implementation. Algorithms 17(1): 24 (2024) - [j11]Lorenzo Valleggi
Learning Bayesian networks with heterogeneous agronomic data sets via mixed-effect models and hierarchical clustering. Eng. Appl. Artif. Intell. 131: 107867 (2024) - 2023
- [c11]Alice Bernasconi, Alessio Zanga, Peter J. F. Lucas, Marco Scutari, Fabio Antonio Stella:
Towards a Transportable Causal Network Model Based on Observational Healthcare Data. HC@AIxIA 2023: 67-82 - [c10]Alessio Zanga
Causal Discovery with Missing Data in a Multicentric Clinical Study. AIME 2023: 40-44 - [c9]Alessandro Bregoli, Karin Rathsman, Marco Scutari, Fabio Stella, Søren Wengel Mogensen:
Analyzing Complex Systems with Cascades Using Continuous-Time Bayesian Networks. TIME 2023: 8:1-8:21 - [i14]Marco Scutari:
fairml: A Statistician's Take on Fair Machine Learning Modelling. CoRR abs/2305.02009 (2023) - [i13]Alessio Zanga, Alice Bernasconi, Peter J. F. Lucas, Hanny Pijnenborg, Casper Reijnen, Marco Scutari, Fabio Stella:
Risk Assessment of Lymph Node Metastases in Endometrial Cancer Patients: A Causal Approach. CoRR abs/2305.10041 (2023) - [i12]Alessio Zanga, Alice Bernasconi, Peter J. F. Lucas, Hanny Pijnenborg, Casper Reijnen, Marco Scutari, Fabio Stella:
Causal Discovery with Missing Data in a Multicentric Clinical Study. CoRR abs/2305.10050 (2023) - [i11]Lorenzo Valleggi, Marco Scutari, Federico Mattia Stefanini:
Learning Bayesian Networks with Heterogeneous Agronomic Data Sets via Mixed-Effect Models and Hierarchical Clustering. CoRR abs/2308.06399 (2023) - [i10]Alessandro Bregoli, Karin Rathsman, Marco Scutari, Fabio Stella, Søren Wengel Mogensen:
Analyzing Complex Systems with Cascades Using Continuous-Time Bayesian Networks. CoRR abs/2308.10606 (2023) - [i9]Alice Bernasconi, Alessio Zanga, Peter J. F. Lucas, Marco Scutari, Fabio Stella:
Towards a Transportable Causal Network Model Based on Observational Healthcare Data. CoRR abs/2311.08427 (2023) - [i8]Marco Scutari:
Entropy and the Kullback-Leibler Divergence for Bayesian Networks: Computational Complexity and Efficient Implementation. CoRR abs/2312.01520 (2023) - 2022
- [j10]Matteo Delucchi
Bayesian network analysis reveals the interplay of intracranial aneurysm rupture risk factors. Comput. Biol. Medicine 147: 105740 (2022) - [j9]Laura Azzimonti
A Bayesian hierarchical score for structure learning from related data sets. Int. J. Approx. Reason. 142: 248-265 (2022) - [j8]Marco Scutari
Achieving fairness with a simple ridge penalty. Stat. Comput. 32(5): 77 (2022) - [c8]Alessio Zanga, Alice Bernasconi, Peter J. F. Lucas, Hanny Pijnenborg, Casper Reijnen, Marco Scutari, Fabio Stella:
Risk Assessment of Lymph Node Metastasis in Endometrial Cancer Patients: A Causal Approach. HC@AIxIA 2022: 1-15 - [c7]Marco Scutari, Christopher Marquis, Laura Azzimonti
Using Mixed-Effects Models to Learn Bayesian Networks from Related Data Sets. PGM 2022: 73-84 - [i7]Marco Scutari:
Comments on: "Hybrid Semiparametric Bayesian Networks". CoRR abs/2205.05910 (2022) - [i6]Marco Scutari, Christopher Marquis, Laura Azzimonti
Using Mixed-Effect Models to Learn Bayesian Networks from Related Data Sets. CoRR abs/2206.03743 (2022) - 2021
- [j7]Alessandro Bregoli
A constraint-based algorithm for the structural learning of continuous-time Bayesian networks. Int. J. Approx. Reason. 138: 105-122 (2021) - [j6]Tjebbe Bodewes
Learning Bayesian networks from incomplete data with the node-average likelihood. Int. J. Approx. Reason. 138: 145-160 (2021) - [i5]Marco Scutari, Manuel Proissl:
Achieving Fairness with a Simple Ridge Penalty. CoRR abs/2105.13817 (2021) - 2020
- [j5]Andrea Ruggieri, Francesco Stranieri
Hard and Soft EM in Bayesian Network Learning from Incomplete Data. Algorithms 13(12): 329 (2020) - [c6]Laura Azzimonti, Giorgio Corani, Marco Scutari:
Structure Learning from Related Data Sets with a Hierarchical Bayesian Score. PGM 2020: 5-16 - [c5]Tjebbe Bodewes, Marco Scutari:
Identifiability and Consistency of Bayesian Network Structure Learning from Incomplete Data. PGM 2020: 29-40 - [c4]Alessandro Bregoli, Marco Scutari, Fabio Stella:
Constraing-Based Learning for Continous-Time Bayesian Networks. PGM 2020: 41-52 - [i4]Alessandro Bregoli
Constraint-Based Learning for Continuous-Time Bayesian Networks. CoRR abs/2007.03248 (2020) - [i3]Laura Azzimonti, Giorgio Corani, Marco Scutari:
Structure Learning from Related Data Sets with a Hierarchical Bayesian Score. CoRR abs/2008.01683 (2020) - [i2]Andrea Ruggieri, Francesco Stranieri, Fabio Stella, Marco Scutari:
Hard and Soft EM in Bayesian Network Learning from Incomplete Data. CoRR abs/2012.05269 (2020)
2010 – 2019
- 2019
- [j4]Marco Scutari, Catharina Elisabeth Graafland, José Manuel Gutiérrez:
Who learns better Bayesian network structures: Accuracy and speed of structure learning algorithms. Int. J. Approx. Reason. 115: 235-253 (2019) - [j3]Marco Scutari
Learning Bayesian networks from big data with greedy search: computational complexity and efficient implementation. Stat. Comput. 29(5): 1095-1108 (2019) - 2018
- [c3]Marco Scutari, Catharina Elisabeth Graafland, José Manuel Gutiérrez:
Who Learns Better Bayesian Network Structures: Constraint-Based, Score-based or Hybrid Algorithms? PGM 2018: 416-427 - 2017
- [c2]Marco Scutari:
Dirichlet Bayesian Network Scores and the Maximum Entropy Principle. AMBN 2017: 8-20 - 2016
- [c1]Marco Scutari:
An Empirical-Bayes Score for Discrete Bayesian Networks. Probabilistic Graphical Models 2016: 438-448 - 2015
- [p2]Marco Scutari
Personalised Medicine: Taking a New Look at the Patient. Foundations of Biomedical Knowledge Representation 2015: 139-141 - [p1]Marco Scutari
Graphical Modelling in Genetics and Systems Biology. Foundations of Biomedical Knowledge Representation 2015: 143-158 - 2014
- [i1]Marco Scutari:
Bayesian Network Constraint-Based Structure Learning Algorithms: Parallel and Optimised Implementations in the bnlearn R Package. CoRR abs/1406.7648 (2014) - 2013
- [j2]Marco Scutari
Identifying significant edges in graphical models of molecular networks. Artif. Intell. Medicine 57(3): 207-217 (2013)
2000 – 2009
- 2009
- [j1]Shweta S. Chavan, Michael A. Bauer
NATbox: a network analysis toolbox in R. BMC Bioinform. 10(S-11): 14 (2009)
Coauthor Index
aka: Fabio Antonio Stella
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last updated on 2024-10-07 21:12 CEST by the dblp team
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