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Related Experiment Video

Updated: May 15, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Model averaging strategies for structure learning in Bayesian networks with limited data.

Bradley M Broom¹, Kim-Anh Do, Devika Subramanian

¹Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, Texas 77030, USA. bmbroom@mdanderson.org

BMC Bioinformatics

|January 17, 2013

Summary

For limited data, Bayesian bagging with the Dirichlet Prior Scoring Metric (DPSM) is the most effective Bayesian network learning strategy. Selecting the single best network from each bootstrap resample outperforms averaging, especially with small datasets.

Related Experiment Videos

Last Updated: May 15, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Area of Science:

Computational Biology
Machine Learning
Statistical Modeling

Background:

Learning Bayesian network structures from data is crucial.
Model averaging with bootstrap replicates and feature selection is common.
Limited data presents challenges for existing methods.

Purpose of the Study:

Evaluate scoring functions for Bayesian network model averaging.
Assess the impact of bootstrap discreteness bias.
Determine optimal strategies for learning from limited data.

Main Methods:

Systematic study on ALARM and INSURANCE benchmarks.
Bayesian bagging with Dirichlet Prior Scoring Metric (DPSM).
Permutation-based method for feature selection thresholds.

Main Results:

Dirichlet Prior Scoring Metric and Bayesian Dirichlet metric are best.
Correcting bootstrap bias harms performance; single best network is superior.
Bayesian bagging with DPSM is most effective for limited data.

Conclusions:

The proposed Bayesian bagging approach significantly outperforms prior methods on small datasets.
Demonstrated application to Glioblastoma multiforme gene expression data reveals survival-related covariates and gene clusters.