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 -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.

• Type: Conference
• Year: 2003
• Where: UAI
• Authors: Changhe Yuan, Marek J. Druzdzel
• Keywords: Bayesian Networks, Importance Sampling Algorithm, Stochastic Sampling Algorithms, UAI 2003, UAI 2008