Automated construction of sparse Bayesian networks from unstructured probabilistic models and domain information

  • Srinivas S
  • Russell S
  • Agogino A
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An algorithm for automated construction of a sparse Bayesian network given an unstructured probabilistic model and causal domain information from an expert has been developed and implemented. The goal is to obtain a network that explicitly reveals as much information regarding conditional independence as possible. The network is built incrementally adding one node at a time. The expert's information and a greedy heuristic that tries to keep the number of arcs added at each step to a minimum are used to guide the search for the next node to add. The probabilistic model is a predicate that can answer queries about independencies in the domain. In practice the model can be implemented in various ways. For example, the model could be a statistical independence test operating on empirical data or a deductive prover operating on a set of independence statements about the domain. © 1990 Elsevier B.V.

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  • Sampath Srinivas

  • Stuart Russell

  • Alice Agogino

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