ABSTRACT
In solving an engineering problem, such as developing empirical models for manufacturing processes, real data of continuous quantities are required. However, the information and/or data obtained from different sources are often clouded by different kinds of uncertainty. The available data of manufacturing problems are frequently imprecise and incomplete. To overcome such problems, fuzzy concepts along with probability theory, e.g., the Bayesian approach, can be adopted and applied to provide meaningful description that bridges the gap between real data and empirical process models.
