2012/06/13 by Gogate, Vibhav, Dechter, Rina
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences
paper · doi:10.48550/arxiv.1206.3232
The paper introduces AND/OR importance sampling for probabilistic graphical models. In contrast to importance sampling, AND/OR importance sampling caches samples in the AND/OR space and then extracts a new sample mean from the stored samples. We prove that AND/OR importance sampling may have lower variance than importance sampling; thereby providing a theoretical justification for preferring it over importance sampling. Our empirical evaluation demonstrates that AND/OR importance sampling is far more accurate than importance sampling in many cases.