Author Information

Shoufan Fang
George Z. Gertner

Abstract

Traditionally, uncertainty analysis of complex simulation models has been conducted based on the assumption of that the components of the model are independent. In practice, correlation is universal in ecosystems. This study applied Bayesian estimation and rejection sampling to generate correlated random samples for an uncertainty analysis of a process based forest growth model, a pipe model. Comparison of error budgets built using independent and correlated distributions shows that correlated distributions are very important to provide reasonable and realistic simulation and uncertainty analysis.

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Apr 30th, 1:00 PM

UNCERTAINTY ANALYSIS OF A PIPE MODEL BASED ON CORRELATED DISTRIBUTIONS

Traditionally, uncertainty analysis of complex simulation models has been conducted based on the assumption of that the components of the model are independent. In practice, correlation is universal in ecosystems. This study applied Bayesian estimation and rejection sampling to generate correlated random samples for an uncertainty analysis of a process based forest growth model, a pipe model. Comparison of error budgets built using independent and correlated distributions shows that correlated distributions are very important to provide reasonable and realistic simulation and uncertainty analysis.