Result: Towards combining probabilistic and interval uncertainty in engineering calculations : Algorithms for computing statistics under interval uncertainty, and their computational complexity
Title:
Towards combining probabilistic and interval uncertainty in engineering calculations : Algorithms for computing statistics under interval uncertainty, and their computational complexity
Authors:
Source:
Reliable Engineering ComputingReliable computing. 12(6):471-501
Publisher Information:
Heidelberg: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 52 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Informatique théorique, Theoretical computing, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, Analyse statistique, Statistical analysis, Análisis estadístico, Approche probabiliste, Probabilistic approach, Enfoque probabilista, Arithmétique intervalle, Interval arithmetic, Aritmética intervalo, Autocorrélation, Autocorrelation, Autocorrelación, Complexité calcul, Computational complexity, Complejidad computación, Corrélation, Correlation, Correlación, Fonction autocorrélation, Autocorrelation function, Función autocorrelación, Modélisation, Modeling, Modelización, Pollution, Polución, Variance, Variancia
Document Type:
Conference
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
NASA Pan-American Center for Earth and Environmental Studies (PACES), University of Texas, El Paso, TX 79968, United States
Applied Biomathematics, 100 North Country Road, Setauket, NY 11733, United States
Dept. of Ecology and Evolution, State University of New York, Stony Brook, NY 11794, United States
Applied Biomathematics, 100 North Country Road, Setauket, NY 11733, United States
Dept. of Ecology and Evolution, State University of New York, Stony Brook, NY 11794, United States
ISSN:
1385-3139
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.18342210
Database:
PASCAL Archive
Further Information
In many engineering applications, we have to combine probabilistic and interval uncertainty. For example, in environmental analysis, we observe a pollution level x(t) in a lake at different moments of time t, and we would like to estimate standard statistical characteristics such as mean, variance, autocorrelation, correlation with other measurements. In environmental measurements, we often only measure the values with interval uncertainty. We must therefore modify the existing statistical algorithms to process such interval data. In this paper, we provide a survey of algorithms for computing various statistics under interval uncertainty and their computational complexity. The survey includes both known and new algorithms.