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Learning the Structure of Bayesian Networks: A Quantitative Assessment of the Effect of Different Algorithmic Schemes
Joint Authors
Castelli, Mauro
Henriques, Roberto
Beretta, Stefano
Gonçalves, Ivo
Ramazzotti, Daniele
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-09-12
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
One of the most challenging tasks when adopting Bayesian networks (BNs) is the one of learning their structure from data.
This task is complicated by the huge search space of possible solutions and by the fact that the problem is NP-hard.
Hence, a full enumeration of all the possible solutions is not always feasible and approximations are often required.
However, to the best of our knowledge, a quantitative analysis of the performance and characteristics of the different heuristics to solve this problem has never been done before.
For this reason, in this work, we provide a detailed comparison of many different state-of-the-art methods for structural learning on simulated data considering both BNs with discrete and continuous variables and with different rates of noise in the data.
In particular, we investigate the performance of different widespread scores and algorithmic approaches proposed for the inference and the statistical pitfalls within them.
American Psychological Association (APA)
Beretta, Stefano& Castelli, Mauro& Gonçalves, Ivo& Henriques, Roberto& Ramazzotti, Daniele. 2018. Learning the Structure of Bayesian Networks: A Quantitative Assessment of the Effect of Different Algorithmic Schemes. Complexity،Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1132931
Modern Language Association (MLA)
Beretta, Stefano…[et al.]. Learning the Structure of Bayesian Networks: A Quantitative Assessment of the Effect of Different Algorithmic Schemes. Complexity No. 2018 (2018), pp.1-12.
https://search.emarefa.net/detail/BIM-1132931
American Medical Association (AMA)
Beretta, Stefano& Castelli, Mauro& Gonçalves, Ivo& Henriques, Roberto& Ramazzotti, Daniele. Learning the Structure of Bayesian Networks: A Quantitative Assessment of the Effect of Different Algorithmic Schemes. Complexity. 2018. Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1132931
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1132931