cutset effect in Bayesian networks
نویسنده
چکیده
The paper investigates the behavior of iterative belief propagation algorithm (IBP) in Bayesian networks with loops. In multiply-connected network, IBP is only guaranteed to converge in linear time to the correct posterior marginals when evidence nodes form a loop-cutset. We propose an -cutset criteria that IBP will converge and compute posterior marginals close to correct when a single value in the domain of each loop-cutset node receives very strong support compared to other values thus producing an effect similar to the observed loop-cutset. We investigate the support for this criteria analytically and empirically and show that it is consistent with previous observations of IBP performance in multiply-connected networks.
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