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Propagating imprecise probabilities through event trees

Gert De Cooman UGent, Filip Hermans and Erik Quaeghebeur (2007) Interuniversity Attraction Pole IAP VI/4 Kickoff meeting, Posters.
abstract
Event trees are a graphical model of a set of possible situations and the possible paths going through them, from the initial situation to the terminal situations. With each situation, there is associated a local uncertainty model that represents beliefs about the next situation. The uncertainty models can be classical, precise probabilities; they can also be of a more general, imprecise probabilistic type, in which case they can be seen as sets of classical probabilities (yielding probability intervals). To work with such event trees, we must combine these local uncertainty models. We show this can be done efficiently by back-propagation through the tree, both for precise and imprecise probabilistic models, and we illustrate this using an imprecise probabilistic counterpart of the classical Markov chain. This allows us to perform a robustness analysis for Markov chains very efficiently.
Please use this url to cite or link to this publication:
author
organization
year
type
conference
publication status
published
subject
in
Interuniversity Attraction Pole IAP VI/4 Kickoff meeting, Posters
conference name
Interuniversity Attraction Pole IAP VI/4 Kickoff meeting
conference location
Louvain-la-Neuve, Belgium
conference start
2007-04-16
conference end
2007-04-16
language
English
UGent publication?
yes
classification
C3
additional info
Event website: http://sites.uclouvain.be/dysco/archive/StudyDays/2007/April16.htm
copyright statement
I have retained and own the full copyright for this publication
id
1974570
handle
http://hdl.handle.net/1854/LU-1974570
date created
2011-12-26 15:46:02
date last changed
2016-12-19 15:37:19
@inproceedings{1974570,
  abstract     = {Event trees are a graphical model of a set of possible situations and the possible paths going through them, from the initial situation to the terminal situations. With each situation, there is associated a local uncertainty model that represents beliefs about the next situation. The uncertainty models can be classical, precise probabilities; they can also be of a more general, imprecise probabilistic type, in which case they can be seen as sets of classical probabilities (yielding probability intervals). To work with such event trees, we must combine these local uncertainty models. We show this can be done efficiently by back-propagation through the tree, both for precise and imprecise probabilistic models, and we illustrate this using an imprecise probabilistic counterpart of the classical Markov chain. This allows us to perform a robustness analysis for Markov chains very efficiently.},
  author       = {De Cooman, Gert and Hermans, Filip and Quaeghebeur, Erik},
  booktitle    = {Interuniversity Attraction Pole IAP VI/4 Kickoff meeting, Posters},
  language     = {eng},
  location     = {Louvain-la-Neuve, Belgium},
  title        = {Propagating imprecise probabilities through event trees},
  year         = {2007},
}

Chicago
De Cooman, Gert, Filip Hermans, and Erik Quaeghebeur. 2007. “Propagating Imprecise Probabilities Through Event Trees.” In Interuniversity Attraction Pole IAP VI/4 Kickoff Meeting, Posters.
APA
De Cooman, Gert, Hermans, F., & Quaeghebeur, E. (2007). Propagating imprecise probabilities through event trees. Interuniversity Attraction Pole IAP VI/4 Kickoff meeting, Posters. Presented at the Interuniversity Attraction Pole IAP VI/4 Kickoff meeting.
Vancouver
1.
De Cooman G, Hermans F, Quaeghebeur E. Propagating imprecise probabilities through event trees. Interuniversity Attraction Pole IAP VI/4 Kickoff meeting, Posters. 2007.
MLA
De Cooman, Gert, Filip Hermans, and Erik Quaeghebeur. “Propagating Imprecise Probabilities Through Event Trees.” Interuniversity Attraction Pole IAP VI/4 Kickoff Meeting, Posters. 2007. Print.