Approximating MAP Inference in Credal Networks Using Probability-Possibility Transformations

This paper focuses on belief graphical models and provides an efficient approximation of MAP inference in credal networks using probability-possibility transformations. We first present two transformations from credal networks to possibilistic ones that are suitable for MAP inference in credal networks. Then we present four criteria to evaluate our approximate MAP inference. The last part of the paper provides experimental studies that compare our approach with both standard exact and approximate MAP inference in credal networks. The paper also provides a brief analysis of MAP inference complexity using possibilistic networks and the results definitely open new perspectives for MAP inference in credal networks.