Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Computational Advantages of Relevance Reasoning in Bayesian Belief
  Networks   This paper introduces a computational framework for reasoning in Bayesian
belief networks that derives significant advantages from focused inference and
relevance reasoning. This framework is based on d -separation and other simple
and computationally efficient techniques for pruning irrelevant parts of a
network. Our main contribution is a technique that we call relevance-based
decomposition. Relevance-based decomposition approaches belief updating in
large networks by focusing on their parts and decomposing them into partially
overlapping subnetworks. This makes reasoning in some intractable networks
possible and, in addition, often results in significant speedup, as the total
time taken to update all subnetworks is in practice often considerably less
than the time taken to update the network as a whole. We report results of
empirical tests that demonstrate practical significance of our approach.

16
The categories of this paper are: stat
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math