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The development of autonomous agents operating in dynamic and stochastic environments requires theories and models of how beliefs and intentions are revised while taking their interplay into account. In this paper, we initiate the study of belief and intention revision in stochastic environments, where an agent's beliefs and intentions are specified in a decidable probabilistic temporal logic. We then provide general Katsuno & Mendelzon-style representation theorems for both belief and intention revision, giving clear semantic characterizations of revision methods.