  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.
