Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Optimizing causal orderings for generating DAGs from data
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual 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
Computational Intelligence for Optimization
Computational Intelligence for Optimization
The Journal of Machine Learning Research
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The structure of a Bayesian network depends heavily on the ordering of its variables: given any ordering it is always possible to build a Bayesian network whose arcs are consistent with the initial ordering; however, the topology of the network, and therefore the number of conditional independence relationships that may be explicitly represented can vary greatly from one ordering to another. As a sparse representation is always preferable to a denser representation of the same model, the task of determining the ordering giving rise to the network with minimum number of arcs is important. In this work we propose methods to obtain a good approximation to the optimal ordering, using only partial information. More precisely, we only use conditional independence relationships of order zero and one, and search for the ordering which best preserves this information. The search process will be guided by genetic algorithms and simulated annealing.