Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A tutorial on learning with Bayesian networks
Learning in graphical models
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Sequential Model Criticism in Probabilistic Expert Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A transformational characterization of equivalent Bayesian network structures
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Causal inference and causal explanation with background knowledge
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Using Bayesian networks to analyze expression data
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
On the quantification of e-business capacity
Proceedings of the 3rd ACM conference on Electronic Commerce
Modeling dependencies in protein-DNA binding sites
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Revising regulatory networks: from expression data to linear causal models
Journal of Biomedical Informatics
Learning causal networks from microarray data
WISB '06 Proceedings of the 2006 workshop on Intelligent systems for bioinformatics - Volume 73
Ensembling Bayesian network structure learning on limited data
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Bayesian Network Structure Ensemble Learning
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Journal of Biomedical Informatics
Learning robust cell signalling models from high throughput proteomic data
International Journal of Bioinformatics Research and Applications
Probabilistic Methods for Bioinformatics: with an Introduction to Bayesian Networks
Probabilistic Methods for Bioinformatics: with an Introduction to Bayesian Networks
The Journal of Machine Learning Research
Efficiently approximating Markov tree bagging for high-dimensional density estimation
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Multiple hypothesis testing and quasi essential graph for comparing two sets of bayesian networks
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
Unsupervised active learning in large domains
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Being Bayesian about network structure
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Integrating marine species biomass data by modelling functional knowledge
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Multilevel Bayesian networks for the analysis of hierarchical health care data
Artificial Intelligence in Medicine
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In recent years there has been significant progress in algorithms and methods for inducing Bayesian networks from data. However, in complex data analysis problems, we need to go beyond being satisfied with inducing networks with high scores. We need to provide confidence measures on features of these networks: Is the existence of an edge between two nodes warranted? Is the Markov blanket of a given node robust? Can we say something about the ordering of the variables? We should be able to address these questions, even when the amount of data is not enough to induce a high scoring network. In this paper we propose Efron's Bootstrap as a computationally efficient approach for answering these questions. In addition, we propose to use these confidence measures to induce better structures from the data, and to detect the presence of latent variables.