Effect of Epistemic Uncertainty in Markovian Reliability Models

Hiroyuki Okamura (), Junjun Zheng (), Tadashi Dohi () and Kishor S. Trivedi ()
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Hiroyuki Okamura: Hiroshima University
Junjun Zheng: Ritsumeikan University
Tadashi Dohi: Hiroshima University
Kishor S. Trivedi: Duke University

A chapter in System Dependability and Analytics, 2023, pp 371-392 from Springer

Abstract: Abstract This chapter introduces the moment-based epistemic uncertainty propagation in Markov models. The epistemic uncertainty in Markov models introduces the uncertainty of model parameters, and it can be propagated by regarding parameters as random variables. The idea behind the moment-based approach is to approximate the multiple integration with a series expansion of model parameters. This leads to the efficient computation of the uncertainty in the expected output measure. The expected output measure is represented by the expected value and the variance of model parameters and the first and second derivatives of output measure with respect to model parameters. In this chapter, we introduce the formulation of moment-based epistemic uncertainty propagation and the concrete methods to obtain the first and second derivatives of output measures in Markov models.

Keywords: Epistemic uncertainty propagation; Markov model; Reliability evaluation (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-3-031-02063-6_22

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DOI: 10.1007/978-3-031-02063-6_22

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