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

Overview of attention for article published in BMC Bioinformatics, August 2012
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Title
Model averaging strategies for structure learning in Bayesian networks with limited data
Published in
BMC Bioinformatics, August 2012
DOI 10.1186/1471-2105-13-s13-s10
Pubmed ID
Authors

Bradley M Broom, Kim-Anh Do, Devika Subramanian

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?

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 2%
United States 1 2%
Poland 1 2%
Unknown 38 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 29%
Researcher 6 15%
Student > Master 4 10%
Student > Bachelor 3 7%
Lecturer 3 7%
Other 8 20%
Unknown 5 12%
Readers by discipline Count As %
Computer Science 7 17%
Medicine and Dentistry 5 12%
Engineering 4 10%
Mathematics 4 10%
Psychology 2 5%
Other 14 34%
Unknown 5 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 September 2012.
All research outputs
#7,762,554
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#3,175
of 4,576 outputs
Outputs of similar age
#71,001
of 126,410 outputs
Outputs of similar age from BMC Bioinformatics
#22
of 33 outputs
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