An Importance Sampling Algorithm Based on Evidence Pre-propagation

Precision achieved by stochastic sampling algorithms for Bayesian networks typically deteriorates in face of extremely unlikely evidence. To address this problem, we propose the Evidence Pre-propagation Importance Sampling algorithm (EPIS-BN), an importance sampling algorithm that computes an approximate importance function using two techniques: loopy belief propagation [19, 25] and e-cutoff heuristic [2]. We tested the performance of EPIS-BN on three large real Bayesian networks: ANDES [3], CPCS [21], and PATHFINDER[11]. We observed that on each of these networks the EPIS-BN algorithm outperforms AISBN [2], the current state of the art algorithm, while avoiding its costly learning stage.

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