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

Troffaes, Matthias; Coolen, Frank

Abstract

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.

Citation

Troffaes, M., & Coolen, F. (2009). Applying the imprecise Dirichlet model in cases with partial observations and dependencies in failure data. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 50(2), 257-268. https://doi.org/10.1016/j.ijar.2008.03.013

• Journal Article Type: Article
• Publication Date: Feb 1, 2009
• Deposit Date: Jun 19, 2009
• Publicly Available Date: Oct 17, 2014
• Journal: International Journal of Approximate Reasoning
• Print ISSN: 0888-613X
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 50
• Issue: 2
• Pages: 257-268
• DOI: https://doi.org/10.1016/j.ijar.2008.03.013
• Keywords: Imprecise Dirichlet model, Independence, Selection bias, Partial observations, Bayesian inference, Robustness.
• Public URL: https://durham-repository.worktribe.com/output/1550082