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- Title
Model averaging strategies for structure learning in Bayesian networks with limited data.
- Authors
Broom, Bradley M; Do, Kim-Anh; Subramanian, Devika
- Abstract
Considerable progress has been made on algorithms for learning the structure of Bayesian networks from data. Model averaging by using bootstrap replicates with feature selection by thresholding is a widely used solution for learning features with high confidence. Yet, in the context of limited data many questions remain unanswered. What scoring functions are most effective for model averaging? Does the bias arising from the discreteness of the bootstrap significantly affect learning performance? Is it better to pick the single best network or to average multiple networks learnt from each bootstrap resample? How should thresholds for learning statistically significant features be selected?
- Publication
BMC bioinformatics, 2012, Vol 13 Suppl 13, pS10
- ISSN
1471-2105
- Publication type
Journal Article
- DOI
10.1186/1471-2105-13-S13-S10