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The authors present a junction tree decomposition based algorithm for parallel exact inference. This is a novel parallel exact inference method for evidence propagation in an arbitrary junction tree. If multiple cliques contain evidence, the performance of any state-of-the-art parallel inference algorithm achieving logarithmic time performance is adversely affected. In this paper, they propose a new approach to overcome this problem. They decompose a junction tree into a set of chains. Cliques in each chain are partially updated after the evidence propagation. These partially updated cliques are then merged in parallel to obtain fully updated cliques. They derive the formula for merging partially updated cliques and estimate the computation workload of each step.
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