Improving Statistical Model Checking by Optimizing Probability Estimation

The core message of this article is to propose several fundamental improvements to the statistical methods used in state-of-the-art statistical model checking (SMC) algorithms for Markov decision processes (MDPs). The authors focus on improving the estimation of transition probabilities, which is a crucial step in model-based SMC, by employing stronger statistical techniques and exploiting structural information about the MDP and the property of interest.