Propagation of Interval and Probabilistic Uncertainty in Cyberinfrastructure-Related Data Processing and Data Fusion

ISBN-13: 9783319126272 / Angielski / Twarda / 2014 / 112 str.

Christian Servin; Vladik Kreinovich

Opis

Based on various examples ranging from geosciences to environmental sciences this book explains how to generate an adequate description of uncertainty, how to justify semi-heuristic algorithms for processing uncertainty, and how to make these algorithms more computationally efficient. It explains in what sense the existing approach to uncertainty as a combination of random and systematic components is only an approximation and presents a more adequate three-component model with an additional periodic error component and how the existing uncertainty propagation techniques can be extended to this model. It provides a justification for a practically efficient heuristic technique; namely, for a technique based on fuzzy decision-making; and explains how the computational complexity of processing uncertainty can be reduced. It also shows that all these methods are based on the idealized assumption that we have a good description of the uncertainty of the original data.It shows that in real life the description of uncertainty is often only approximate, the presented algorithms for uncertainty propagation are not well-justified and not very computationally efficient, and describes how this uncertainty information can be extracted from the data in the practice case.

Spis treści

Introduction.- Towards a More Adequate Description of Uncertainty.- Towards Justification of Heuristic Techniques for Processing Uncertainty.- Towards More Computationally Efficient Techniques for Processing Uncertainty.- Towards Better Ways of Extracting Information About Uncertainty from Data.

Blurb

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, and how to make these algorithms

more computationally efficient. It explains in what sense the existing approach to

uncertainty as a combination of random and systematic components is only an

approximation, presents a more adequate three-component model with an additional

periodic error component, and 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, and, on several practical examples, explains how to extract the missing

information about uncertainty from the available data.