Standard approaches for inference in probabilistic formalisms with first-order constructs include lifted variable elimination (LVE) for single queries, boiling down to computing marginal distributions. To handle multiple queries efficiently, the lifted junction tree algorithm (LJT) uses a first-order cluster representation of a knowledge base and LVE in its computations. Another type of query asks for a most probable explanation (MPE) for given events. The purpose of this paper is twofold: (i) We formalise how to compute an MPE in a lifted way with LVE and LJT. (ii) We present a case study in the area of IT security for risk analysis. A lifted computation of MPEs exploits symmetries, while providing a correct and exact result equivalent to one computed on ground level.
CITATION STYLE
Braun, T., & Möller, R. (2018). Lifted Most Probable Explanation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10872 LNAI, pp. 39–54). Springer Verlag. https://doi.org/10.1007/978-3-319-91379-7_4
Mendeley helps you to discover research relevant for your work.