Approximating Credal Network Inferences by Linear Programming

  • Alessandro Antonucci
  • Cassio P. de Campos
  • David Huber
  • Marco Zaffalon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7958)


An algorithm for approximate credal network updating is presented. The problem in its general formulation is a multilinear optimization task, which can be linearized by an appropriate rule for fixing all the local models apart from those of a single variable. This simple idea can be iterated and quickly leads to very accurate inferences. The approach can also be specialized to classification with credal networks based on the maximality criterion. A complexity analysis for both the problem and the algorithm is reported together with numerical experiments, which confirm the good performance of the method. While the inner approximation produced by the algorithm gives rise to a classifier which might return a subset of the optimal class set, preliminary empirical results suggest that the accuracy of the optimal class set is seldom affected by the approximate probabilities.


Bayesian Network Extreme Point Local Model Linear Constraint Approximate Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alessandro Antonucci
    • 1
  • Cassio P. de Campos
    • 1
  • David Huber
    • 1
  • Marco Zaffalon
    • 1
  1. 1.Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA)Manno-LuganoSwitzerland

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