Probabilistic model checking extends traditional model checking by incorporating quantitative information about the probability of system transitions. However, probabilistic models that describe interesting behavior are often too complex for straightforward analysis. Abstraction is one way to deal with this complexity: instead of analyzing the ("concrete") model, a simpler ("abstract") model that preserves the relevant properties is built and analyzed. This paper surveys various abstraction techniques proposed in the past decade. For each abstraction technique we identify in what sense properties are preserved or provide alternatively suitable boundaries. 2 Related Work 2.1 Literature Abstraction is of immense importance for the analysis of large probabilistic systems. Consequently, the field has been studied extensively. One of the most popular techniques is bisimulation minimization [4]. Here, the states of the abstract
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