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Scalable, Efficient and Correct Learning of Markov Boundaries Under the Faithfulness Assumption

https://doi.org/10.1007/11518655_13
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-1-4612-2748-9openalex-snapshot
Causation, Prediction, and Search
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Learning Bayesian Networks
inferredcites →https://doi.org/10.1023/a:1007465528199openalex-snapshot
Bayesian Network Classifiers
inferredcites →https://doi.org/10.1093/bioinformatics/bti1137openalex-snapshot
Growing Bayesian network models of gene networks from seed genes
inferredcites →https://doi.org/10.1145/1281192.1327961openalex-snapshot
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inferredcites →https://doi.org/10.1145/507515.507523openalex-snapshot
KDD Cup 2001 report
inferredcites →https://doi.org/10.1145/956750.956838openalex-snapshot
Time and sample efficient discovery of Markov blankets and direct causal relations
inferredcites →https://doi.org/10.13140/2.1.1125.2169openalex-snapshot
Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Markov Blanket Ranking Using Kernel-Based Conditional Dependence Measures
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A Study on the Evolution of Bayesian Network Graph Structures
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Robust Gene Selection from Microarray Data with a Novel Markov Boundary Learning Method: Application to Diabetes Analysis
inferred← citeshttps://doi.org/10.1007/978-3-642-03915-7_18openalex-snapshot
Incremental Bayesian Network Learning for Scalable Feature Selection
inferred← citeshttps://doi.org/10.1007/978-3-642-41398-8_34openalex-snapshot
Dynamic MMHC: A Local Search Algorithm for Dynamic Bayesian Network Structure Learning
inferred← citeshttps://doi.org/10.1007/s13042-025-02908-2openalex-snapshot
A algorithm based on approximate skeleton of Bayesian network structure learning
inferred← citeshttps://doi.org/10.1016/j.compbiolchem.2007.08.005openalex-snapshot
A hybrid Bayesian network learning method for constructing gene networks
inferred← citeshttps://doi.org/10.1016/j.eswa.2025.129249openalex-snapshot
Dependency-based anomaly detection: A general framework and comprehensive evaluation
inferred← citeshttps://doi.org/10.1016/j.ijar.2006.06.008openalex-snapshot
Towards scalable and data efficient learning of Markov boundaries
inferred← citeshttps://doi.org/10.1016/j.ijar.2008.02.007openalex-snapshot
Adapting Bayes network structures to non-stationary domains
inferred← citeshttps://doi.org/10.1016/j.knosys.2023.110334openalex-snapshot
Research on the multi-source causal feature selection method based on multiple causal relevance
inferred← citeshttps://doi.org/10.1093/bioinformatics/bti1137openalex-snapshot
Growing Bayesian network models of gene networks from seed genes
inferred← citeshttps://doi.org/10.1109/icdm.2008.82openalex-snapshot
A Conservative Feature Subset Selection Algorithm with Missing Data
inferred← citeshttps://doi.org/10.1109/tnnls.2022.3178075openalex-snapshot
Causal Feature Selection With Dual Correction
inferred← citeshttps://doi.org/10.1142/9781860948732_0017openalex-snapshot
AN INFORMATION THEORETIC METHOD FOR RECONSTRUCTING LOCAL REGULATORY NETWORK MODULES FROM POLYMORPHIC SAMPLES
inferred← citeshttps://doi.org/10.1145/3409382openalex-snapshot
Causality-based Feature Selection
inferred← citeshttps://doi.org/10.11591/telkomnika.v11i8.3079openalex-snapshot
A Method of Discovering Tolerance Markov Blanket Based on Completely Dependent Unknown Components
inferred← citeshttps://doi.org/10.12677/csa.2026.164112openalex-snapshot
A Survey on Causal Discovery Algorithms
inferred← citeshttps://doi.org/10.3390/math12182927openalex-snapshot
A Novel Ensemble Method of Divide-and-Conquer Markov Boundary Discovery for Causal Feature Selection
inferred← citeshttps://doi.org/10.48550/arxiv.2011.06716openalex-snapshot
Dependency-based Anomaly Detection: a General Framework and Comprehensive Evaluation
inferred← citeshttps://doi.org/10.5555/1756006.1756013openalex-snapshot
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation
inferred← citeshttps://doi.org/10.70675/3ac1ec8dzc1d3z4c86z8f4az35efaf9791eaopenalex-snapshot
Développement de stratégies avancées pour l'identification de relations épistatiques dans les études d'associations génotype-phénotype
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Apprentissage fédéré de réseaux bayésiens préservant la confidentialité dans le cadre d’applications médicales personnalisées
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