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Bayesian networks have been widely used as knowledge models in business, engineering, biomedicine, and so on. When a network is learned with incomplete knowledge, the numerical model based on probability theory needs to be extended. This study presents a robust approach for diagnosis from Bayesian networks with fuzzy parameters. A simulation algorithm is designed to answer the queries from the models. The formulation of piecewise linear possibility distribution functions maintain the scalability in exact approaches. [ABSTRACT FROM AUTHOR] more...