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The CONEstrip algorithm

Erik Quaeghebeur UGent (2012) Synergies of Soft Computing and Statistics for Intelligent Data Analysis/Advances in Intelligent Systems and Computing. 190. p.45-54
abstract
Uncertainty models such as sets of desirable gambles and (conditional) lower previsions can be represented as convex cones. Checking the consistency of and drawing inferences from such models requires solving feasibility and optimization problems. We consider finitely generated such models. For closed cones, we can use linear programming; for conditional lower prevision-based cones, there is an efficient algorithm using an iteration of linear programs. We present an efficient algorithm for general cones that also uses an iteration of linear programs.
Please use this url to cite or link to this publication:
author
organization
year
type
conference
publication status
published
subject
keyword
PROBABILITY, linear programming, inference, feasibility, convex cones, consistency
in
Synergies of Soft Computing and Statistics for Intelligent Data Analysis/Advances in Intelligent Systems and Computing
editor
Rudolf Kruse, Michael R Berthold, Christian Moewes, María Ángeles Gil, Przemysław Grzegorzewski and Olgierd Hryniewicz
volume
190
pages
45 - 54
publisher
Springer
place of publication
Berlin, Germany
conference name
6th International conference on Soft Methods in probability and Statistics
conference location
Konstanz, Germany
conference start
2012-10-04
conference end
2012-10-06
Web of Science type
Proceedings Paper
Web of Science id
000312969600006
ISSN
2194-5357
ISBN
9783642330414
DOI
10.1007/978-3-642-33042-1_6
language
English
UGent publication?
yes
classification
P1
copyright statement
I have transferred the copyright for this publication to the publisher
id
3007274
handle
http://hdl.handle.net/1854/LU-3007274
date created
2012-10-05 10:49:40
date last changed
2015-06-17 10:15:50
@inproceedings{3007274,
  abstract     = {Uncertainty models such as sets of desirable gambles and (conditional) lower previsions can be represented as convex cones. Checking the consistency of and drawing inferences from such models requires solving feasibility and optimization problems. We consider finitely generated such models. For closed cones, we can use linear programming; for conditional lower prevision-based cones, there is an efficient algorithm using an iteration of linear programs. We present an efficient algorithm for general cones that also uses an iteration of linear programs.},
  author       = {Quaeghebeur, Erik},
  booktitle    = {Synergies of Soft Computing and Statistics for Intelligent Data Analysis/Advances in Intelligent Systems and Computing},
  editor       = {Kruse, Rudolf and Berthold, Michael R and Moewes, Christian  and Gil, Mar{\'i}a {\'A}ngeles and Grzegorzewski, Przemys\unmatched{0142}aw and Hryniewicz, Olgierd },
  isbn         = {9783642330414},
  issn         = {2194-5357},
  keyword      = {PROBABILITY,linear programming,inference,feasibility,convex cones,consistency},
  language     = {eng},
  location     = {Konstanz, Germany},
  pages        = {45--54},
  publisher    = {Springer},
  title        = {The CONEstrip algorithm},
  url          = {http://dx.doi.org/10.1007/978-3-642-33042-1\_6},
  volume       = {190},
  year         = {2012},
}

Chicago
Quaeghebeur, Erik. 2012. “The CONEstrip Algorithm.” In Synergies of Soft Computing and Statistics for Intelligent Data Analysis/Advances in Intelligent Systems and Computing, ed. Rudolf Kruse, Michael R Berthold, Christian Moewes, María Ángeles Gil, Przemysław Grzegorzewski, and Olgierd Hryniewicz, 190:45–54. Berlin, Germany: Springer.
APA
Quaeghebeur, E. (2012). The CONEstrip algorithm. In Rudolf Kruse, M. R. Berthold, C. Moewes, M. Á. Gil, P. Grzegorzewski, & O. Hryniewicz (Eds.), Synergies of Soft Computing and Statistics for Intelligent Data Analysis/Advances in Intelligent Systems and Computing (Vol. 190, pp. 45–54). Presented at the 6th International conference on Soft Methods in probability and Statistics, Berlin, Germany: Springer.
Vancouver
1.
Quaeghebeur E. The CONEstrip algorithm. In: Kruse R, Berthold MR, Moewes C, Gil MÁ, Grzegorzewski P, Hryniewicz O, editors. Synergies of Soft Computing and Statistics for Intelligent Data Analysis/Advances in Intelligent Systems and Computing. Berlin, Germany: Springer; 2012. p. 45–54.
MLA
Quaeghebeur, Erik. “The CONEstrip Algorithm.” Synergies of Soft Computing and Statistics for Intelligent Data Analysis/Advances in Intelligent Systems and Computing. Ed. Rudolf Kruse et al. Vol. 190. Berlin, Germany: Springer, 2012. 45–54. Print.