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Applying the imprecise Dirichlet model in cases with partial observations and dependencies in failure data.

Troffaes, Matthias and Coolen, Frank (2009) 'Applying the imprecise Dirichlet model in cases with partial observations and dependencies in failure data.', International journal of approximate reasoning., 50 (2). pp. 257-268.


Imprecise probabilistic methods in reliability provide exciting opportunities for dealing with partial observations and incomplete knowledge on dependencies in failure data. In this paper, we explore the use of the imprecise Dirichlet model for dealing with such information, and we derive both exact results and bounds which enable analytical investigations. However, we only consider a very basic two-component system, as analytical solutions for larger systems will become very complex. We explain how the results are related to similar analyses under data selection or reporting bias, and we discuss some challenges for future research.

Item Type:Article
Keywords:Imprecise Dirichlet model, Independence, Selection bias, Partial observations, Bayesian inference, Robustness.
Full text:(AM) Accepted Manuscript
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Publisher statement:NOTICE: this is the author’s version of a work that was accepted for publication in International Journal of Approximate Reasoning. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Approximate Reasoning, 50, 2, 2009, 10.1016/j.ijar.2008.03.013.
Date accepted:No date available
Date deposited:17 October 2014
Date of first online publication:February 2009
Date first made open access:No date available

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