A mixture model-based nonparametric approach to estimating a count distribution

Chee, Chew-Seng

doi:10.1016/j.csda.2016.11.012

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…Non-probabilistic approaches, such as interval methods [ 20 , 21 , 22 ], fuzzy theory [ 23 ] and convex model theory [ 24 ], and probabilistic methods, such as the maximum likelihood estimation method [ 25 ], Bayesian method [ 26 , 27 ], stochastic inverse method [ 28 ], non-parametric minimum power method [ 29 ], and probabilistic neural networks [ 30 ] have been presented in the existing literature.…”

Section: Literature Review Of Uncertainty Quantificationmentioning

confidence: 99%

Role of Sensors in Error Propagation with the Dynamic Constrained Observability Method

Tian

Nogal

Casas

et al. 2021

Sensors

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The inverse problem of structural system identification is prone to ill-conditioning issues; thus, uniqueness and stability cannot be guaranteed. This issue tends to amplify the error propagation of both the epistemic and aleatory uncertainties, where aleatory uncertainty is related to the accuracy and the quality of sensors. The analysis of uncertainty quantification (UQ) is necessary to assess the effect of uncertainties on the estimated parameters. A literature review is conducted in this paper to check the state of existing approaches for efficient UQ in the parameter identification field. It is identified that the proposed dynamic constrained observability method (COM) can make up for some of the shortcomings of existing methods. After that, the COM is used to analyze a real bridge. The result is compared with the existing method, demonstrating its applicability and correct performance by a reinforced concrete beam. In addition, during the bridge system identification by COM, it is found that the best measurement set in terms of the range will depend on whether the epistemic uncertainty involved or not. It is concluded that, because the epistemic uncertainty will be removed as the knowledge of the structure increases, the optimum sensor placement should be achieved considering not only the accuracy of sensors, but also the unknown structural part.

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