Credal SumProduct Networks
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Proceedings of the Tenth International Symposium on Imprecise Probability: Theories and Applications, PMLR 62:205216, 2017.
Abstract
Sumproduct networks are a relatively new and increasingly popular class of (precise) probabilistic graphical models that allow for marginal inference with polynomial effort. As with other probabilistic models, sumproduct networks are often learned from data and used to perform classification. Hence, their results are prone to be unreliable and overconfident. In this work, we develop credal sumproduct networks, an imprecise extension of sumproduct networks. We present algorithms and complexity results for common inference tasks. We apply our algorithms on realistic classification task using images of digits and show that credal sumproduct networks obtained by a perturbation of the parameters of learned sumproduct networks are able to distinguish between reliable and unreliable classifications with high accuracy.
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