Techniques for Efficient Monte Carlo Simulation. Volume 3. Variance Reduction

McGrath, Elgie J.; Irving, D.C.

doi:10.21236/ad0762723

Cited by 16 publications

(15 citation statements)

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“…The analysis of variance reduction techniques by decomposing them into memDownloaded by [Michigan State University] at 05:22 03 January 2015 bers of the six classes of transformations is central. Other attempts to develop a taxonomy of variance reduction, such as McGrath and Irving (1973) and Wilson (1983aWilson ( , 1985, were not completely successful, in part because they tried to categorize VRTs. But as we have seen in Section 4, simple partitions do not exist.…”

Section: Discussionmentioning

confidence: 99%

Decomposition of some weil-known variance reduction techniques^†

Nelson

Schmeiser

1986

Journal of Statistical Computation and Simulation

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Section: Discussionmentioning

confidence: 99%

Decomposition of some weil-known variance reduction techniques^†

Nelson

Schmeiser

1986

Journal of Statistical Computation and Simulation

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“…Compared to other numerical methods, the Monte Carlo method is efficient with regard to computing time and easy to implement and understand. Using Monte Carlo methods for simulating the propagation of input errors through model predictions was initiated by O'Neill [9] and McGrath and Irving [10].…”

Section: Monte Carlo Analysismentioning

confidence: 99%

Using Monte Carlo techniques to judge model prediction accuracy: Validation of the pesticide root zone model 3.12

Warren-Hicks¹,

Carbone

Havens³

2002

Enviro Toxic and Chemistry

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Individuals from the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA) Environmental Model Validation Task Force (FEMVTF) Statistics Committee periodically met to discuss the mechanism for conducting an uncertainty analysis of Version 3.12 of the pesticide root zone model (PRZM 3.12) and to identify those model input parameters that most contribute to model prediction error. This activity was part of a larger project evaluating PRZM 3.12. The goal of the uncertainty analysis was to compare site-specific model predictions and field measurements using the variability in each as a basis of comparison. Monte Carlo analysis was used as an integral tool for judging the model's ability to predict accurately. The model was judged on how well it predicts measured values, taking into account the uncertainty in the model predictions. Monte Carlo analysis provides the tool for inferring model prediction uncertainty. We argue that this is a fairer test of the model than a simple one-to-one comparison between predictions and measurements. Because models are known to be imperfect predictors prior to running the model, the inaccuracy in model predictions should be considered when models are judged for their predictive ability. Otherwise, complex models can easily fail a validation test. Few complex models, such as PRZM 3.12, would pass a typical model validation exercise. This paper describes the approaches to the validation of PRZM 3.12 used by the committee and discusses issues in sampling distribution selection and appropriate statistics for interpreting the model validation results.

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“…Naturally this method is no match for the most sophisticated Gamma-samplers [4] but it is remarkable that in the case of Maxwell-distribution its efficiency is at least not less than that of the method used in reactor physical calculations [5], while its programming is much simpler.…”

Section: E=~ ~ Gz-l(t)dt E-xdx=~ E-2*xx-ldx/f(~)=~ E-'tz-ldt/2zf(2)mentioning

confidence: 99%

Another special method to sample probability density functions

Lux¹

1979

Computing

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--ZusammenfassungAnother Special Method to Sample Probability Density Functions. A modified rejection technique is proposed, to sample certain probability density functions. The general drawing up of the method and some applications, specific to reactor physical Monte Carlo calculations, are given. In a special case the efficiency of the new method is compared to that of the conventional rejection technique.Eine weitere spezielle Methode flit die Modellierung yon Wahrscheinlichkeitsdichtefunktionen. Eine modifizierte Rejektionstechnik zur Modellierung einiger Wahrscheinlichkeitsdichten wird vorgeschlagen. Die Grundztige der Methode sowie einige Anwendungen sind mit spezieller Rticksicht auf die Monte Carlo Rechnungen der Reaktorphysik angegeben. In einem Spezialfalle wird die Wirkungsffihigkeit der neuen Methode mit der der konventionellen Rejektionstechnik vergliehen.

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Techniques for Efficient Monte Carlo Simulation. Volume 3. Variance Reduction

Abstract: Office of Maral Research (Code 462) DepartoMflt of the Navy ArUivtoa, Virginia 22217 Under Contract N00014-72-C-0293 ^[^riCU SUTi^KT Appiov.d for public rel«M«. P'tribuaoo ÜBlimif^ '

Cited by 16 publications

References 0 publications

Decomposition of some weil-known variance reduction techniques^†

Decomposition of some weil-known variance reduction techniques^†

Using Monte Carlo techniques to judge model prediction accuracy: Validation of the pesticide root zone model 3.12

Another special method to sample probability density functions

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Techniques for Efficient Monte Carlo Simulation. Volume 3. Variance Reduction

Abstract: Office of Maral Research (Code 462) DepartoMflt of the Navy ArUivtoa, Virginia 22217 Under Contract N00014-72-C-0293 ^[^riCU SUTi^KT Appiov.d for public rel«M«. P'tribuaoo ÜBlimif^ '

Cited by 16 publications

References 0 publications

Decomposition of some weil-known variance reduction techniques†

Decomposition of some weil-known variance reduction techniques†

Using Monte Carlo techniques to judge model prediction accuracy: Validation of the pesticide root zone model 3.12

Another special method to sample probability density functions

Contact Info

Product

Resources

About

Decomposition of some weil-known variance reduction techniques^†

Decomposition of some weil-known variance reduction techniques^†