國立高雄大學圖資館 |

Language: English

Back

Propagation of interval and probabil...

Kreinovich, Vladik.

Propagation of interval and probabilistic uncertainty in cyberinfrastructure-related data processing and data fusion

Record Type:	Electronic resources : Monograph/item
Title/Author:	Propagation of interval and probabilistic uncertainty in cyberinfrastructure-related data processing and data fusionby Christian Servin, Vladik Kreinovich.
Author:	Servin, Christian.
other author:	Kreinovich, Vladik.
Published:	Cham :Springer International Publishing :2015.
Description:	viii, 112 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Uncertainty (Information theory)
Online resource:	http://dx.doi.org/10.1007/978-3-319-12628-9
ISBN:	9783319126289 (electronic bk.)

Propagation of interval and probabilistic uncertainty in cyberinfrastructure-related data processing and data fusion
Servin, Christian.

Propagation of interval and probabilistic uncertainty in cyberinfrastructure-related data processing and data fusion[electronic resource] /by Christian Servin, Vladik Kreinovich. - Cham :Springer International Publishing :2015. - viii, 112 p. :ill., digital ;24 cm. - Studies in systems, decision and control,v.152198-4182 ;. - Studies in systems, decision and control ;v.3..

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.

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.

ISBN: 9783319126289 (electronic bk.)

Standard No.: 10.1007/978-3-319-12628-9doiSubjects--Topical Terms:

206405
Uncertainty (Information theory)

LC Class. No.: Q375

Dewey Class. No.: 003.54

Propagation of interval and probabilistic uncertainty in cyberinfrastructure-related data processing and data fusion
LDR:02428nmm a2200325 a 4500 001 460585
003 DE-He213
005 20150714154425.0
006 m d
007 cr nn 008maaau
008 151110s2015 gw s 0 eng d
020 $a 9783319126289 (electronic bk.)
020 $a 9783319126272 (paper)
024 7 $a 10.1007/978-3-319-12628-9 $2 doi
035 $a 978-3-319-12628-9
040 $a GP $c GP
041 0 $a eng
050 4 $a Q375
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 003.54 $2 23
090 $a Q375 $b .S492 2015
100 1 $a Servin, Christian. $3 712108
245 1 0 $a Propagation of interval and probabilistic uncertainty in cyberinfrastructure-related data processing and data fusion $h [electronic resource] / $c by Christian Servin, Vladik Kreinovich.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a viii, 112 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in systems, decision and control, $x 2198-4182 ; $v v.15
505 0 $a 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.
520 $a 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.
650 0 $a Uncertainty (Information theory) $3 206405
650 0 $a Cyberinfrastructure. $3 456002
650 1 4 $a Engineering. $3 210888
650 2 4 $a Computational Intelligence. $3 338479
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 348605
700 1 $a Kreinovich, Vladik. $3 610822
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Studies in systems, decision and control ; $v v.3. $3 678532
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-12628-9
950 $a Engineering (Springer-11647)