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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.
Keywords
feasibility, inference, convex cones, consistency, linear programming, PROBABILITY

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Chicago
Quaeghebeur, Erik. 2013. “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. (2013). 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; 2013. 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, 2013. 45–54. Print.
@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      = {feasibility,inference,convex cones,consistency,linear programming,PROBABILITY},
  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         = {2013},
}

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