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Supplementary material for the SBES 2023 paper "Bayesian Networks for Software Engineering: A Tertiary Study".
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创建时间：
2023-07-22
相关数据集
Supplementary data-BNs.xlsx
Node probability tables for the Bayesian networks models of different SSM approaches.
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Partition MCMC for Inference on Acyclic Digraphs
Acyclic digraphs are the underlying representation of Bayesian networks, a widely used class of probabilistic graphical models. Learning the underlying graph from data is a way of gaining insights abo
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Inference results of Boolean network (Best-Fit with optimal discretization k-means and maxK = 2), Dynamic Bayesian Network (with least squares estimation, and default parameters alpha1 = 0.5 and alpha2 = 0.05 for first order dependencies and full dependencies correspondingly) and Boolean Function Network (maxTimeDelay = 10 and p1 = 0.005).
Inference results of Boolean network (Best-Fit with optimal discretization k-means and maxK = 2), Dynamic Bayesian Network (with least squares estimation, and default parameters alpha1 = 0.5 and alpha
NIAID Data Ecosystem40
Data from: Benthic ecosystem cascade effects in Antarctica using Bayesian network inference
Data from: Benthic ecosystem cascade effects in Antarctica using Bayesian network inference. This data was originally published in Brasier et al. 2018. Brasier, MJ, Grant, SM, Trathan, PN, Allcock,
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Supplemental Files.zip
Supplemental files associated with the article entitled "Using Diagnostic Classification Models to Validate Attribute Hierarchies and Evaluate Model Fit in Bayesian Networks"
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