Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Adaptive Probabilistic Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Stochastic Local Algorithms for Learning Belief Networks: Searching in the Space of the Orderings
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Journal of Artificial Intelligence Research
Comparing Bayesian network classifiers
UAI'99 Proceedings of the Fifteenth 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
Induction of selective Bayesian classifiers
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
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In this paper we explore the use of several types of structural restrictions within algorithms for learning Bayesian networks. These restrictions may codify expert knowledge in a given domain, in such a way that a Bayesian network representing this domain should satisfy them. Our objective is to study whether the algorithms for automatically learning Bayesian networks from data can benefit from this prior knowledge to get better results. We formally define three types of restrictions: existence of arcs and/or edges, absence of arcs and/or edges, and ordering restrictions, and also study their interactions and how they can be managed within Bayesian network learning algorithms based on the score+search paradigm. Then we particularize our study to the classical local search algorithm with the operators of arc addition, arc removal and arc reversal, and carry out experiments using this algorithm on several data sets.