Relevance for robust Bayesian network MAP-explanations
Publication date
2022-10
Authors
Renooij, Silja
Editors
Salmeron, Antonio
Rumi, Rafael
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taverne
Abstract
In the context of explainable AI, the concept of MAP-independence was recently introduced as a means for conveying the (ir)relevance of intermediate nodes for MAP computations in Bayesian networks. In this paper, we further study the concept of MAP-independence, discuss methods for finding sets of relevant nodes, and suggest ways to use these in providing users with an explanation concerning the robustness of the MAP result.
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Citation
Renooij, S 2022, Relevance for robust Bayesian network MAP-explanations. in A Salmeron & R Rumi (eds), Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR. Proceedings of Machine Learning Research, vol. 186, MLResearchPress, pp. 13-24, International Conference on Probabilistic Graphical Models, Almeria, Spain, 5/10/22. < https://proceedings.mlr.press/v186/renooij22a.html >, conference