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Incremental Junction Tree Inference

Hamza Agli 1 Philippe Bonnard 2 Christophe Gonzales 1 Pierre-Henri Wuillemin 1 
LIP6 - Laboratoire d'Informatique de Paris 6
2 IBM France Lab [Biot]
IBM - Paris [Bois-Colombes]
Abstract : Performing probabilistic inference in multi-target dynamic systems is a challenging task. When the system, its evidence and/or its targets evolve, most of the inference algorithms either recompute everything from scratch, even though incremental changes do not invalidate all the previous computations, or do not fully exploit incrementality to minimize computations. This incurs strong unnecessary overheads when the system under study is large. To alleviate this problem, we propose in this paper a new junction tree-based message-passing inference algorithm that, given a new query, minimizes computations by identifying precisely the set of messages that differ from the preceding computations. Experimental results highlight the efficiency of our approach.
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Submitted on : Wednesday, July 13, 2016 - 4:19:49 PM
Last modification on : Sunday, June 26, 2022 - 9:41:32 AM


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Hamza Agli, Philippe Bonnard, Christophe Gonzales, Pierre-Henri Wuillemin. Incremental Junction Tree Inference. IPMU16, Jun 2016, Eindhoven, Netherlands. ⟨10.1007/978-3-319-40596-4_28⟩. ⟨hal-01345418⟩



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