The max-min hill-climbing Bayesian network structure learning algorithm

Ioannis Tsamardinos; Laura E. Brown; Constantin Aliferis

doi:https://doi.org/10.1007/s10994-006-6889-7

The max-min hill-climbing Bayesian network structure learning algorithm

2006 · Machine Learning · 1,862 citations · 1 from inside this corpus

Ioannis Tsamardinos, Laura E. Brown, Constantin Aliferis low

We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and search-and-score techniques in a principled and effective way. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. In our extensive empirical evaluation MMHC outperforms on average and in terms of various metrics several prototypical and state-of-the-art algorithms, namely the PC, Sparse Candidate, Three Phase Dependency Analysis, Optimal Reinsertion, Greedy Equivalence Search, and Greedy Search. These are the first empirical results simultaneously comparing most of the major Bayesian network algorithms against each other. MMHC offers certain theoretical advantages, specifically over the Sparse Candidate algorithm, corroborated by our experiments. MMHC and detailed results of our study are publicly available at http://www.dsl-lab.org/supplements/mmhc_paper/mmhc_index.html.

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Learning Bayesian Networks with the bnlearn R Package→The max-min hill-climbing Bayesian network structure learning algorithmLegacy edge — source detail unavailable
The max-min hill-climbing Bayesian network structure learning algorithm→Probabilistic Reasoning in Intelligent Systems: Networks of Plausible InferenceLegacy edge — source detail unavailable
The max-min hill-climbing Bayesian network structure learning algorithm→Modern Information RetrievalLegacy edge — source detail unavailable
The max-min hill-climbing Bayesian network structure learning algorithm→CausalityLegacy edge — source detail unavailable
The max-min hill-climbing Bayesian network structure learning algorithm→Causality: Models, Reasoning and InferenceLegacy edge — source detail unavailable
The max-min hill-climbing Bayesian network structure learning algorithm→Causation, Prediction, and SearchLegacy edge — source detail unavailable
The max-min hill-climbing Bayesian network structure learning algorithm→An Introduction to Variational Methods for Graphical ModelsLegacy edge — source detail unavailable
The max-min hill-climbing Bayesian network structure learning algorithm→A Bayesian Method for the Induction of Probabilistic Networks from DataLegacy edge — source detail unavailable
The max-min hill-climbing Bayesian network structure learning algorithm→Probabilistic Reasoning in Intelligent SystemsLegacy edge — source detail unavailable
The max-min hill-climbing Bayesian network structure learning algorithm→Learning Bayesian Networks: The Combination of Knowledge and Statistical DataLegacy edge — source detail unavailable
The max-min hill-climbing Bayesian network structure learning algorithm→A Bayesian method for the induction of probabilistic networks from dataLegacy edge — source detail unavailable
The max-min hill-climbing Bayesian network structure learning algorithm→Learning Bayesian networks: The combination of knowledge and statistical dataLegacy edge — source detail unavailable
The max-min hill-climbing Bayesian network structure learning algorithm→Learning Bayesian NetworksLegacy edge — source detail unavailable
An Introduction to Variational Methods for Graphical Models→Probabilistic Reasoning in Intelligent Systems: Networks of Plausible InferenceLegacy edge — source detail unavailable
Learning Bayesian networks: The combination of knowledge and statistical data→Causation, Prediction, and SearchLegacy edge — source detail unavailable
Learning Bayesian networks: The combination of knowledge and statistical data→A Bayesian Method for the Induction of Probabilistic Networks from DataLegacy edge — source detail unavailable
Learning Bayesian networks: The combination of knowledge and statistical data→Probabilistic Reasoning in Intelligent Systems: Networks of Plausible InferenceLegacy edge — source detail unavailable
Learning Bayesian networks: The combination of knowledge and statistical data→A Bayesian method for the induction of probabilistic networks from dataLegacy edge — source detail unavailable
Causation, Prediction, and Search→A Bayesian Method for the Induction of Probabilistic Networks from DataLegacy edge — source detail unavailable
A Bayesian Method for the Induction of Probabilistic Networks from Data→A Bayesian method for the induction of probabilistic networks from dataLegacy edge — source detail unavailable
A Bayesian Method for the Induction of Probabilistic Networks from Data→Probabilistic Reasoning in Intelligent SystemsLegacy edge — source detail unavailable
Learning Bayesian Networks with the bnlearn R Package→Causation, Prediction, and SearchLegacy edge — source detail unavailable
Learning Bayesian Networks with the bnlearn R Package→A Bayesian Method for the Induction of Probabilistic Networks from DataLegacy edge — source detail unavailable
Learning Bayesian Networks with the bnlearn R Package→Learning Bayesian NetworksLegacy edge — source detail unavailable
Learning Bayesian Networks with the bnlearn R Package→Probabilistic Reasoning in Intelligent Systems: Networks of Plausible InferenceLegacy edge — source detail unavailable
Learning Bayesian Networks with the bnlearn R Package→Learning Bayesian Networks: The Combination of Knowledge and Statistical DataLegacy edge — source detail unavailable
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data→Causation, Prediction, and SearchLegacy edge — source detail unavailable
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data→A Bayesian Method for the Induction of Probabilistic Networks from DataLegacy edge — source detail unavailable
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data→Probabilistic Reasoning in Intelligent Systems: Networks of Plausible InferenceLegacy edge — source detail unavailable
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data→A Bayesian method for the induction of probabilistic networks from dataLegacy edge — source detail unavailable
A Bayesian method for the induction of probabilistic networks from data→Probabilistic Reasoning in Intelligent SystemsLegacy edge — source detail unavailable

What this paper cites, inside the corpus

Paper	Year	Cited
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference	1988	16,981
Modern Information Retrieval	1999	11,572
Causality	2009	11,477
Causality: Models, Reasoning and Inference	2001	4,831
Causation, Prediction, and Search	1993	4,748
An Introduction to Variational Methods for Graphical Models	1999	3,793
A Bayesian Method for the Induction of Probabilistic Networks from Data	1992	3,530
Probabilistic Reasoning in Intelligent Systems	1988	3,339
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data	1995	3,262
A Bayesian method for the induction of probabilistic networks from data	1992	2,348
Learning Bayesian networks: The combination of knowledge and statistical data	1995	2,045
Learning Bayesian Networks	2017	1,448

What cites it, inside the corpus

Paper	Year	Cited
Learning Bayesian Networks with the bnlearn R Package	2010	1,769

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Topics

Bayesian Modeling and Causal Inference	Computer Science
Data Mining Algorithms and Applications	Computer Science
Rough Sets and Fuzzy Logic	Computer Science

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