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We consider a class of hybrid systems that involve random phenomena, in addition to discrete and continuous behaviour. Examples of such systems include wireless sensing and control applications. We propose and compare two abstraction techniques for this class of models, which yield lower and upper bounds on the optimal probability of reaching a particular class of states. We also demonstrate the applicability of these abstraction techniques to the computation of long-run average rewarddoi:10.1109/qest.2011.17 dblp:conf/qest/HahnNPWZ11 fatcat:fdwygmpuqndovkxcu7jdlw4euq