Monte Carlo evaluation of biological variation: Random generation of correlated non-Gaussian model parameters

摘要

When modelling the behaviour of horticultural products, demonstrating large Sources of biological variation, We often run into the issue of non-Gaussian distributed model parameters. This work presents in algorithm to reproduce such correlated non-Gaussian model parameters for Use with Monte Carlo simulations. This algorithm works around the problem of non-Gaussian distributions by transforming the observed non-Gaussian probability distributions using I proposed SKN-distribution function before applying the covariance decomposition algorithm to genetate Gaussian random co-varying parameter sets. The proposed SKN-distribution function is based oil the standard Gaussian distribution function and can exhibit different degrees of both skewness and kurtosis. This technique is demonstrated using a case Study oil modelling the ripening of tomato fruit evaluating the propagation of biological variation with time. (C) 2007 Elsevier B.V. All rights reserved.