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Notes: Approximating Precision in Simulation Projections: An Efficient Alternative to Monte Carlo Methods

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Estimates of the precision of predictions made with simulation models are often desired to gauge the variability of predictions, place confidence intervals around the predictions, and test hypotheses. However, estimates of precision are not usually calculated for large multicomponent simulation models because of the huge computational costs. To overcome this, the error propagation method can be used to approximate the precision of simulation estimates. The method was used in this study to approximate the variance of predictions made with a variation of the individual tree growth and yield model, STEMS (Stand and Tree Evaluation and Modeling System). The method was compared with a crude Monte Carlo method for obtaining simulation variances. At its worse, the error propagation approximation of standard deviation diverged from the Monte Carlo approximation by 16% for a 50-year projection. The technique reduced execution time for calculating the variances by a factor of almost 2,000. For. Sci. 33(1):230-239.

Keywords: Simulation models; Stand and Tree Evaluation and Modeling System (STEMS); error propagation method

Document Type: Miscellaneous

Affiliations: Associate Professor, Department of Forestry, University of Illinois, Urbana, IL 61801

Publication date: March 1, 1987

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