TY - BOOK ID - 4863516 TI - Propagation of Interval and Probabilistic Uncertainty in Cyberinfrastructure-related Data Processing and Data Fusion AU - Servin, Christian. AU - Kreinovich, Vladik. PY - 2015 SN - 9783319126289 331912627X 9783319126272 3319126288 PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Engineering. KW - Computational Intelligence. KW - Data Mining and Knowledge Discovery. KW - Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. KW - Data mining. KW - Ingénierie KW - Exploration de données (Informatique) KW - Engineering & Applied Sciences KW - Computer Science KW - Uncertainty (Information theory) KW - Cyberinfrastructure. KW - Cyber-based information systems KW - Cyber-infrastructure KW - Measure of uncertainty (Information theory) KW - Shannon's measure of uncertainty KW - System uncertainty KW - Statistics. KW - Computational intelligence. KW - Intelligence, Computational KW - Artificial intelligence KW - Soft computing KW - Statistical analysis KW - Statistical data KW - Statistical methods KW - Statistical science KW - Mathematics KW - Econometrics KW - Algorithmic knowledge discovery KW - Factual data analysis KW - KDD (Information retrieval) KW - Knowledge discovery in data KW - Knowledge discovery in databases KW - Mining, Data KW - Database searching KW - Construction KW - Industrial arts KW - Technology KW - Electronic data processing KW - Information technology KW - Computer networks KW - Computer systems KW - Distributed databases KW - High performance computing KW - Information measurement KW - Probabilities KW - Questions and answers KW - Distributed processing KW - Statistics . UR - https://www.unicat.be/uniCat?func=search&query=sysid:4863516 AB - 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. ER -