Propagation of Interval & Probabilistic Uncertainty in Cyberinfrastructure-related Data Processing & Data Fusion
Springer | Data Mining, Control Systems | Nov. 21 2014 | ISBN-10: 331912627X | 112 pages | pdf | 2.15 mb
On various examples ranging from geosciences to environmental sciences
this book explains how to generate an adequate description of uncertainty, how to justify semiheuristic algorithms for processing uncertainty, & how to make these algorithms more computationally efficient. It explains in what sense the existing approach to uncertainty as a combination of r&om & systematic components is only an approximation, presents a more adequate three-component model with an additional periodic error component, & explains how uncertainty propagation techniques can be extended to this model. The book provides a justification for a practically efficient heuristic technique (based on fuzzy decision-making). It explains how the computational
complexity of uncertainty processing can be reduced. The book also shows how to take into account that in real life, the information about uncertainty is often only partially known, &, on several practical examples, explains how to extract the missing information about uncertainty from the available data.