Lifted Dynamic Junction Tree Algorithm

We work on probabilistic first-order formalisms where the domain objects are known. In these formalisms, the standard approach for inference with first-order constructs include lifted variable elimination (LVE) for single queries. To handle multiple queries efficiently and to obtain a compact representation, the lifted junction tree algorithm (LJT) extends LVE. We extend the formalism and respectively LJT to handle temporal aspects. To be more precise, we combine the advantages of LJT and the interface algorithm in LDJT, which efficiently solves the inference problems filtering and prediction.

Additionally, we are interested in solving other inference problems, e.g. smoothing, and to learn relational temporal models from data.

LDJT is supported by CISCO. The work is carried out as part of Jointlab 1 within the COPICOH center for connected health.



  • Marcel Gehrke, Tanya Braun, Ralf Möller: Lifted Dynamic Junction Tree Algorithm
    to be published in: Proceedings of the International Conference on Conceptual Structures, 2018
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