Evaluating uncertainty in stochastic simulation models

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This paper discusses fundamental concepts of uncertainty analysis relevant to both stochastic simulation models and deterministic models. A stochastic simulation model, called a simulation model, is a stochastic mathematical model that incorporates random numbers in the calculation of the model prediction. Queuing models are familiar simulation models in which random numbers are used for sampling interarrival and service times. Another example of simulation models is found in probabilistic risk assessments where atmospheric dispersion submodels are used to calculate movement of material. For these models, randomness comes not from the sampling of times but from the sampling of weather conditions, which ... continued below

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6 p.

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McKay, M.D. February 1, 1998.

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Description

This paper discusses fundamental concepts of uncertainty analysis relevant to both stochastic simulation models and deterministic models. A stochastic simulation model, called a simulation model, is a stochastic mathematical model that incorporates random numbers in the calculation of the model prediction. Queuing models are familiar simulation models in which random numbers are used for sampling interarrival and service times. Another example of simulation models is found in probabilistic risk assessments where atmospheric dispersion submodels are used to calculate movement of material. For these models, randomness comes not from the sampling of times but from the sampling of weather conditions, which are described by a frequency distribution of atmospheric variables like wind speed and direction as a function of height above ground. A common characteristic of simulation models is that single predictions, based on one interarrival time or one weather condition, for example, are not nearly as informative as the probability distribution of possible predictions induced by sampling the simulation variables like time and weather condition. The language of model analysis is often general and vague, with terms having mostly intuitive meaning. The definition and motivations for some of the commonly used terms and phrases offered in this paper lead to an analysis procedure based on prediction variance. In the following mathematical abstraction the authors present a setting for model analysis, relate practical objectives to mathematical terms, and show how two reasonable premises lead to a viable analysis strategy.

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6 p.

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OSTI as DE98003354

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  • 2. international symposium on sensitivity analysis of model output, Venice (Italy), 9-12 Apr 1998

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  • Other: DE98003354
  • Report No.: LA-UR--97-4503
  • Report No.: CONF-980424--
  • Grant Number: W-7405-ENG-36
  • Office of Scientific & Technical Information Report Number: 642820
  • Archival Resource Key: ark:/67531/metadc696714

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  • February 1, 1998

Added to The UNT Digital Library

  • Aug. 14, 2015, 8:43 a.m.

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  • May 5, 2016, 6:26 p.m.

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McKay, M.D. Evaluating uncertainty in stochastic simulation models, article, February 1, 1998; New Mexico. (digital.library.unt.edu/ark:/67531/metadc696714/: accessed October 19, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.