An empirical evaluation of sampling methods in risk analysis simulation: quasi-Monte Carlo, descriptive sampling, and latin hypercube sampling

Saliby, Eduardo; Pacheco, Flavio

doi:10.1109/wsc.2002.1166440

Cited by 20 publications

(11 citation statements)

References 8 publications

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“…Successive iterations are compiled and compared to the original calculation to determine the uncertainty associated with variables specific to the internal workings of the Doses-2005 model. Additional options for analysis of the variables inherent to the model could include Latin hypercube sampling (McKay et al 1979) or direct sampling (Saliby 1990), which have demonstrated better performance than conventional Monte Carlo techniques (Saliby 1997;Saliby and Pacheco 2002). However, the actual time savings and variance reduction provided by these options were not significant enough to necessitate the programming and application of a more complex analysis routine for the uncertainties analysis.…”

Section: Uncertainty Analysis Proceduresmentioning

confidence: 99%

Uncertainties Analysis for the Plutonium Dosimetry Model, Doses-2005, Using Mayak Bioassay Data

Bess¹,

Krahenbuhl²,

Miller

et al. 2007

Health Physics

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The Doses-2005 model is a combination of the International Commission on Radiological Protection (ICRP) models modified using data from the Mayak Production Association cohort. Surrogate doses from inhaled plutonium can be assigned to approximately 29% of the Mayak workers using their urine bioassay measurements and other history records. The purpose of this study was to quantify and qualify the uncertainties in the estimates for radiation doses calculated with the Doses-2005 model by using Monte Carlo methods and perturbation theory. The average uncertainty in the yearly dose estimates for most organs was approximately 100% regardless of the transportability classification. The relative source of the uncertainties comes from three main sources: 45% from the urine bioassay measurements, 29% from the Doses-2005 model parameters, and 26% from the reference masses for the organs. The most significant reduction in the overall dose uncertainties would result from improved methods in bioassay measurement with additional improvements generated through further model refinement. Additional uncertainties were determined for dose estimates resulting from changes in the transportability classification and the smoking toggle. A comparison was performed to determine the effect of using the model with data from either urine bioassay or autopsy data; no direct correlation could be established. Analysis of the model using autopsy data and incorporation of results from other research efforts that have utilized plutonium ICRP models could improve the Doses-2005 model and reduce the overall uncertainty in the dose estimates.

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Section: Uncertainty Analysis Proceduresmentioning

confidence: 99%

Uncertainties Analysis for the Plutonium Dosimetry Model, Doses-2005, Using Mayak Bioassay Data

Bess¹,

Krahenbuhl²,

Miller

et al. 2007

Health Physics

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“…Some quantitative techniques, as described by Almeyda and Hinojosa (2001) Carlo sampling in risk analysis application (Lewis 1998;Irwin 2003). Others used the Latin Hypercube sampling technique, as in Saliby and Pacheco (2002), that is employed to value the guarantee in Indonesian toll road project (Wibowo 2004). However, a key challenge in applying this approach is to define appropriate assumptions for underlying risk factors since infrastructure projects are not assets that can be explicitly linked to an observable market price.…”

Section: Introductionmentioning

confidence: 99%

Development of Risk Assessment Framework for Political Risk Guarantee in Public-Private Partnership Projects

Permana

Kong

2018

JIPM

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In Public-Private Partnership (PPP) projects, guarantees can be regarded as a government fiscal support. Due to its uncertainty, one of the key challenges in guarantee valuation is to define appropriate assumptions for underlying risk factors since infrastructure projects are not assets that can be explicitly linked to an observable market price. While very few governments formally measure them, it is also found that sometimes the assessment on political risk covered by the guarantees is highly subjective and ambiguous. This paper proposes a methodology that combined quantitative and qualitative approach for contingent liabilities assessment for PPP infrastructure projects with focus on political risk. It uses fuzzy sets based risk assessment that quantifies expert opinion on likelihood and impact of risk factors into an input to quantitative risk analysis. A particular developing country is chosen so that the proposed methodology can be applied according to the country and infrastructure sectors specific context.

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“…In Ziha [19], DS was applied to IS for the reduction of variance in the probability of failure. It has also been used in experimental design [20,21] and risk analysis simulation [22,23].…”

Section: Introductionmentioning

confidence: 99%

Choosing a suitable sample size in descriptive sampling

2010

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Descriptive sampling (DS) is an alternative to crude Monte Carlo sampling (CMCS) in finding solutions to structural reliability problems. It is known to be an effective sampling method in approximating the distribution of a random variable because it uses the deterministic selection of sample values and their random permutation,. However, because this method is difficult to apply to complex simulations, the sample size is occasionally determined without thorough consideration. Input sample variability may cause the sample size to change between runs, leading to poor simulation results. This paper proposes a numerical method for choosing a suitable sample size for use in DS. Using this method, one can estimate a more accurate probability of failure in a reliability problem while running a minimal number of simulations. The method is then applied to several examples and compared with CMCS and conventional DS to validate its usefulness and efficiency.

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An empirical evaluation of sampling methods in risk analysis simulation: quasi-Monte Carlo, descriptive sampling, and latin hypercube sampling

Cited by 20 publications

References 8 publications

Uncertainties Analysis for the Plutonium Dosimetry Model, Doses-2005, Using Mayak Bioassay Data

Uncertainties Analysis for the Plutonium Dosimetry Model, Doses-2005, Using Mayak Bioassay Data

Development of Risk Assessment Framework for Political Risk Guarantee in Public-Private Partnership Projects

Choosing a suitable sample size in descriptive sampling

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