Evaluating a policy by deploying it in the real world can be risky and costly. Off-policy evaluation (OPE) algorithms use historical data collected from running a previous policy to evaluate a new policy, which provides a means for evaluating a policy without requiring it to ever be deployed. Importance sampling is a popular OPE method because it is robust to partial observability and works wit...

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...

This paper describes a discrete-time retrial queue and shows how importance sampling simulations can be applied for estimating the probability of large orbit content and the overflow fraction of primary calls.