Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Optimizing causal orderings for generating DAGs from data
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Computer-based probabilistic-network construction
Computer-based probabilistic-network construction
Association Models for Web Mining
Data Mining and Knowledge Discovery
Random Generation of Bayesian Networks
SBIA '02 Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Being Bayesian about Network Structure
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Enumerating Markov Equivalence Classes of Acyclic Digraph Models
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Improved learning of Bayesian networks
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
On characterizing Inclusion of Bayesian Networks
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
MAMBO: Discovering Association Rules Based on Conditional Independencies
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Random generation of dags for graph drawing
Random generation of dags for graph drawing
Learning equivalence classes of bayesian-network structures
The Journal of Machine Learning Research
Optimal structure identification with greedy search
The Journal of Machine Learning Research
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A transformational characterization of equivalent Bayesian network structures
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Learning equivalence classes of Bayesian network structures
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Learning Bayesian network structures by searching for the best ordering with genetic algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Exact Bayesian Structure Discovery in Bayesian Networks
The Journal of Machine Learning Research
Approximation Methods for Efficient Learning of Bayesian Networks
Proceedings of the 2008 conference on Approximation Methods for Efficient Learning of Bayesian Networks
Bayesian learning of Bayesian networks with informative priors
Annals of Mathematics and Artificial Intelligence
Bayesian Belief Network for Tsunami Warning Decision Support
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Learning Bayesian network equivalence classes with Ant Colony optimization
Journal of Artificial Intelligence Research
Learning an L1-regularized Gaussian Bayesian network in the equivalence class space
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On local optima in learning bayesian networks
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
MCMC learning of bayesian network models by markov blanket decomposition
ECML'05 Proceedings of the 16th European conference on Machine Learning
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
The inference of breast cancer metastasis through gene regulatory networks
Journal of Biomedical Informatics
Graphical models as surrogates for complex ground motion models
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
A review on evolutionary algorithms in Bayesian network learning and inference tasks
Information Sciences: an International Journal
The Journal of Machine Learning Research
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Two or more Bayesian network structures are Markov equivalent when the corresponding acyclic digraphs encode the same set of conditional independencies. Therefore, the search space of Bayesian network structures may be organized in equivalence classes, where each of them represents a different set of conditional independencies. The collection of sets of conditional independencies obeys a partial order, the so-called "inclusion order." This paper discusses in depth the role that the inclusion order plays in learning the structure of Bayesian networks. In particular, this role involves the way a learning algorithm traverses the search space. We introduce a condition for traversal operators, the inclusion boundary condition, which, when it is satisfied, guarantees that the search strategy can avoid local maxima. This is proved under the assumptions that the data is sampled from a probability distribution which is faithful to an acyclic digraph, and the length of the sample is unbounded. The previous discussion leads to the design of a new traversal operator and two new learning algorithms in the context of heuristic search and the Markov Chain Monte Carlo method. We carry out a set of experiments with synthetic and real-world data that show empirically the benefit of striving for the inclusion order when learning Bayesian networks from data.