BEEM : Bucket Elimination with External Memory
This addresses memory constraints for researchers and practitioners using probabilistic graphical models, though it is incremental as it builds on existing Bucket Elimination methods.
The paper tackles the memory limitation of exact inference algorithms for probabilistic graphical models by extending Bucket Elimination to utilize disk memory, enabling exact solution of large problems previously unsolvable.
A major limitation of exact inference algorithms for probabilistic graphical models is their extensive memory usage, which often puts real-world problems out of their reach. In this paper we show how we can extend inference algorithms, particularly Bucket Elimination, a special case of cluster (join) tree decomposition, to utilize disk memory. We provide the underlying ideas and show promising empirical results of exactly solving large problems not solvable before.