Diagnosis from bayesian networks with fuzzy parameters – a case in supply chains

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.