Refractor Importance Sampling
This addresses a specific computational bottleneck in Bayesian inference for probabilistic reasoning applications, representing an incremental improvement.
The paper tackles the problem of high error variance in Bayesian network importance sampling propagation under evidential reasoning by introducing Refractor Importance Sampling (RIS), which reduces variance by applying localized arc changes to approach the optimal importance function, with empirical validation on synthetic and real-world networks.
In this paper we introduce Refractor Importance Sampling (RIS), an improvement to reduce error variance in Bayesian network importance sampling propagation under evidential reasoning. We prove the existence of a collection of importance functions that are close to the optimal importance function under evidential reasoning. Based on this theoretic result we derive the RIS algorithm. RIS approaches the optimal importance function by applying localized arc changes to minimize the divergence between the evidence-adjusted importance function and the optimal importance function. The validity and performance of RIS is empirically tested with a large setof synthetic Bayesian networks and two real-world networks.