Abstract
The paper describes an approach to representing, aggregating and propagating aleatory and epistemic uncertainty through computational models. The approach was developed in part under the Epistemic Uncertainty Project formed at Sandia National Laboratories, USA, to investigate the applicability and usefulness of some of the modern mathematical theories for the representation of different types of uncertainty, and came out as a result of collaboration with Saint Petersburg Forest Technical Academy, Russia, and personally with L. Utkin. Different aspects of the approach can be found in [1]–[3].
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References
Kozine I.O. and Utkin L.V. Constructing Coherent Interval Statistical Models from Unreliable Judgements. In Proceedings of the Conference ESREL′2001 “Safety and Reliability”, Torino, Italy, September 16-20, 2001, p. 173-180.
Kozine I.O. and Utkin L.V. Processing Unreliable Judgements with an Imprecise Hierarchical Model. Risk. Decision and Policy 2002; vol. 7: 325–339.
Kozine I.O. and Utkin L.V. An approach to combining unreliable pieces of evidence and their propagation in a system response analysis. Reliability Engg. and Systems Safety. To be published in 2004.
Walley P. Statistical reasoning with imprecise probabilities, London: Chapman and Hall, 1991.
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© 2004 Springer-Verlag London
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Kozine, I. (2004). A Hierarchical Uncertainty Model, Combination Rules and Uncertainty Propagation. In: Spitzer, C., Schmocker, U., Dang, V.N. (eds) Probabilistic Safety Assessment and Management. Springer, London. https://doi.org/10.1007/978-0-85729-410-4_403
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DOI: https://doi.org/10.1007/978-0-85729-410-4_403
Publisher Name: Springer, London
Print ISBN: 978-1-4471-1057-6
Online ISBN: 978-0-85729-410-4
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