A chain-model genetic algorithm for Bayesian network structure learning

  title={A chain-model genetic algorithm for Bayesian network structure learning},
  author={Ratiba Kabli and Frank Herrmann and John A. W. McCall},
Bayesian Networks are today used in various fields and domains due to their inherent ability to deal with uncertainty. Learning Bayesian Networks, however is an NP-Hard task [7]. The super exponential growth of the number of possible networks given the number of factors in the studied problem domain has meant that more often, approximate and heuristic rather than exact methods are used. In this paper, a novel genetic algorithm approach for reducing the complexity of Bayesian network structure… CONTINUE READING
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Local computations with probabilities on graphical structures and their application to expert systems

  • S. L. Lauritzen, D. J. Spiegelhalter
  • Journal of the Royal Statistical Society,
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