The binary states, i.e., success or failed state assumptions used in conventional reliability are inappropriate for reliability analysis of complex industrial systems due to lack of sufficient probabilistic information. For large complex systems, the uncertainty of each individual parameter enhances the uncertainty of the system reliability. In this paper, the concept of fuzzy reliability...

Incorporating domain knowledge into the modeling process is an effective way to improve learning accuracy. However, as it is provided by humans, domain knowledge can only be specified with some degree of uncertainty. We propose to explicitly model such uncertainty through probabilistic constraints over the parameter space. In contrast to hard parameter constraints, our approach is effective als...

In this paper the fuzzy structural reliability index was determined through modeling epistemic uncertainty arising from ambiguity in statistical parameters of random variables. The First Order Reliability Method (FORM) has been used and a robust genetic algorithm in the alpha level optimization method has been proposed for the determination of the fuzzy reliability index. The sensitivity level ...

Geospatial predictive models often require mapping of predefined concepts or categories with various conditioning factors in a given space. This chapter discusses various aspects of uncertainty in predictive modeling by characterizing different typologies of classification uncertainty. It argues that understanding uncertainty semantics is a perquisite for efficient handling and management of pr...

In this paper, we claim that software development will do well by explicit modeling of its uncertainties using existing uncertainty modeling techniques. This is accomplished initially by stating the Maxim of Uncertainty in Software Engineering (MUSE), followed by a detailed presentation of uncertainty in software testing. We then propose that a speci c technique, known as Bayesian Belief Networ...

When modeling and solving a realistic optimization problem, we always face uncertainty presenting in the problem, e.g., unknown parameters. The uncertainty makes both the realism and tractability of optimization problems more difficult. In this section, we introduce the formulation with uncertainty and some examples stimulating from real-life decision making request to give a good description o...