KSL-89-17
## Probabilistic Inference in Multiply Connected Brief Networks Using Loop Cutsets

**Reference: **
Suermondt, H. J. &
Cooper, G. F. Probabilistic Inference in Multiply Connected Brief Networks Using Loop Cutsets. 1989.

**Abstract:**
The method of conditioning permits probabilistic inference in multiply
connected belief networks using an algorithm by Pearl. This method uses a
select set of nodes, the loop cutset, to render the multiply connected network
singly connected. We discuss the function of the nodes of the loop cutset and
a condition that must be met by the nodes of the loop cutset. We show that
the problem of finding a loop cutset that optimizes probabilistic inference
using the method of conditioning is NP-hard. We present a heuristic algorithm
for finding a small loop cutset in polynomial times, and we analyze the
performance of this heuristic algorithm empirically.

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