Analytic uncertainty and sensitivity analysis of models with input correlations

Zhu, Yueying; Wang, Qiuping Alexandre; Li, Wei; Cai, X.

doi:10.1016/j.physa.2017.12.041

Cited by 8 publications

(6 citation statements)

References 36 publications

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“…Note that another critical aspect of the PEM (as described above) is that it requires stochastically independent input variables X 1 , … X n . That means if some X i for i = 1 , … , n are correlated, one has to try out other tools 29 or make suitable adjustments to the present approach by, for example, using alternative transformation functions 30,31 (cf. Table 1) before applying the general PEM scheme from equation (8).…”

Section: Resultsmentioning

confidence: 99%

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Analyzing uncertainties in model response using the point estimate method: Applications from railway asset management

Neumann

Dutschk

Schenkendorf

2019

Proceedings of the Institution of Mechanical Engineers, Part O:

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Predicting current and future states of rail infrastructure based on existing data and measurements is essential for optimal maintenance and operation of railway systems. Mathematical models are helpful tools for detecting failures and extrapolating current states into the future. This, however, inherently gives rise to uncertainties in the model response that must be analyzed carefully to avoid misleading results and conclusions. Commonly, Monte Carlo simulations are used for such analyses which often require a large number of sample points to be evaluated for convergence. Moreover, even if quite close to the exact distributions, the Monte Carlo approach necessarily provides approximate results only. In contrast to that, the present contribution reviews an alternative way of computing important statistical quantities of the model response. The so-called point estimate method, which can be shown to be exact under certain constraints, usually (i.e. depending on the number of input variables) works with only a few specific sample points. Thus, the point estimate method helps to reduce the computational load for model evaluation considerably in the case of complex models or large-scale applications. To demonstrate the point estimate method, five academic but typical examples of railway asset management are analyzed in more detail: (a) track degradation, (b) reliability analysis of composite systems, (c) terminal reliability in rail networks, (d) failure detection/identification using decision trees, and (e) track condition modeling incorporating maintenance. Advantages as well as limitations of the point estimate method in comparison with common Monte Carlo simulations are discussed.

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Section: Resultsmentioning

confidence: 99%

“…. , n are correlated, one has to try out other tools 29 or make suitable adjustments to the present approach by, for example, using alternative transformation functions 30,31 (cf. Table 1) before applying the general PEM scheme from equation (8).…”

Section: Resultsmentioning

confidence: 99%

Analyzing uncertainties in model response using the point estimate method: Applications from railway asset management

Neumann

Dutschk

Schenkendorf

2019

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…Each simulation run uses a m = 200. The relative errors are calculated based on the theoretical value σ Y = 0.252 provided in [33]. The proposed method provides more accurate estimation than the benchmark method.…”

Section: -Bar Horizontal Trussmentioning

confidence: 99%

“…The HIV model used in [33] is considered as our fourth example to validate the proposed method. The output of interest is the basic reproduction number (R 0 ), which is arguably regarded as the most important quantity that measures the effectiveness of an infectious disease spreading through a population [34,35].…”

Section: Hiv Modelmentioning

confidence: 99%

Data-Driven Sparse Polynomial Chaos Expansion for Models with Dependent Inputs

Liu¹,

Choe²

2022

SSRN Journal

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“…Robustness analysis can demonstrate the possible model outcomes for individuals in a population where biological parameters can vary greatly between individuals. A robust model shows resilience to changes in model inputs, presenting a more stable model [ 38 , 39 ], although it should be able to account for variances seen in a population. If clinical ranges of observables (such as cell types) are available this analysis can also establish a degree of confidence in the fidelity of model predictions given the variation seen in a population of heterogeneous individuals.…”

Section: Introductionmentioning

confidence: 99%

Uncovering the effects of heterogeneity and parameter sensitivity on within-host dynamics of disease: malaria as a case study

2021

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Background The fidelity and reliability of disease model predictions depend on accurate and precise descriptions of processes and determination of parameters. Various models exist to describe within-host dynamics during malaria infection but there is a shortage of clinical data that can be used to quantitatively validate them and establish confidence in their predictions. In addition, model parameters often contain a degree of uncertainty and show variations between individuals, potentially undermining the reliability of model predictions. In this study models were reproduced and analysed by means of robustness, uncertainty, local sensitivity and local sensitivity robustness analysis to establish confidence in their predictions. Results Components of the immune system are responsible for the most uncertainty in model outputs, while disease associated variables showed the greatest sensitivity for these components. All models showed a comparable degree of robustness but displayed different ranges in their predictions. In these different ranges, sensitivities were well-preserved in three of the four models. Conclusion Analyses of the effects of parameter variations in models can provide a comparative tool for the evaluation of model predictions. In addition, it can assist in uncovering model weak points and, in the case of disease models, be used to identify possible points for therapeutic intervention.

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Analytic uncertainty and sensitivity analysis of models with input correlations

Cited by 8 publications

References 36 publications

Analyzing uncertainties in model response using the point estimate method: Applications from railway asset management

Analyzing uncertainties in model response using the point estimate method: Applications from railway asset management

Data-Driven Sparse Polynomial Chaos Expansion for Models with Dependent Inputs

Uncovering the effects of heterogeneity and parameter sensitivity on within-host dynamics of disease: malaria as a case study

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