Keynote Paper: Parametric Uncertainty Propagation through Dependability Models

被引：5

作者：

Okamura, Hiroyuki ^{[1
]}

Dohi, Tadashi ^{[1
]}

Trivedi, Kishor ^{[2
]}

机构：

[1] Hiroshima Univ, Grad Sch Engn, Dept Informat Engn, 1-4-1 Kagamiyama, Higashihiroshima 7398527, Japan

[2] Duke Univ, Dept ECE, Durham, NC 27708 USA

来源：

2018 EIGHTH LATIN-AMERICAN SYMPOSIUM ON DEPENDABLE COMPUTING (LADC) | 2018年

关键词：

epistemic uncertainty; aleatory uncertainty; uncertainty propagation; moment-based approach; CONFIDENCE-INTERVALS; RELIABILITY; SYSTEM; COMPONENT;

D O I：

10.1109/LADC.2018.00011

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The uncertainty propagation is to investigate the effect of errors in model input parameters on the system output measure in probability models. In this paper, we present a moment-based approach of the uncertainty propagation of model input parameters. The presented approach requires only the fist two moments of model parameters, and has an advantage in terms of computation over the closed-form, numerical and sampling-based approaches for uncertainty propagation. The paper presents the properties of moment-based approach by comparing the existing Bayes estimation for the uncertainty propagation in a simple reliability model. An availability model of a server with virtual machines is used to illustrate the applicability of our method in practical problems.

引用

页码：10 / 18

页数：9

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