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Stochastic channel error models are an important part of performance evaluations of wireless protocols. In fact, for a given protocol, the performance measures of interest seen in simulations often depend on the choice of the error models, simpler models often giving poorer quality performance predictions. The paper presents a new class of Markovian-based channel models, called bipartite models. They allow the user to choose freely the desired model complexity and therefore the model accuracy. We demonstrate through simulations of an example system that this model class gives much more accurate predictions of performance parameters than other popular channel models.