Andere Kunden interessierten sich auch für
![Propagation of Interval and Probabilistic Uncertainty in Cyberinfrastructure-related Data Processing and Data Fusion]( "Propagation of Interval and Probabilistic Uncertainty in Cyberinfrastructure-related Data Processing and Data Fusion")
Propagation of Interval and Probabilistic Uncertainty in Cyberinfrastructure-related Data Processing and Data Fusion74,99 €![Towards Analytical Techniques for Optimizing Knowledge Acquisition, Processing, Propagation, and Use in Cyberinfrastructure and Big Data]( "Towards Analytical Techniques for Optimizing Knowledge Acquisition, Processing, Propagation, and Use in Cyberinfrastructure and Big Data")
Towards Analytical Techniques for Optimizing Knowledge Acquisition, Processing, Propagation, and Use in Cyberinfrastructure and Big Data64,99 €![Beyond Databases, Architectures and Structures]( "Beyond Databases, Architectures and Structures")
Beyond Databases, Architectures and Structures37,99 €![Big Data Analytics]( "Big Data Analytics")
Big Data Analytics75,99 €![Beyond Databases, Architectures, and Structures]( "Beyond Databases, Architectures, and Structures")
Beyond Databases, Architectures, and Structures37,99 €![Proceedings of International Conference on ICT for Sustainable Development]( "Proceedings of International Conference on ICT for Sustainable Development")
Proceedings of International Conference on ICT for Sustainable Development148,99 €![Data Fusion in Information Retrieval]( "Data Fusion in Information Retrieval")
Data Fusion in Information Retrieval110,99 €
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.
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.