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 queries from the graphical models. The formulation of piecewise linear possibility distribution functions maintain the scalability in exact approaches.
diagnosis bayesian networks fuzzy parameters simulation piecewise linear possibility functions