3 papers
stat.ME2019
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…
stat.ME2018
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…
stat.CO2018
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…