Monte Carlo evaluation of biological variation: Random generation of correlated non-Gaussian model parameters |
| |
Authors: | Maarten LATM Hertog Nico ScheerlinckBart M Nicolaï |
| |
Institution: | BIOSYST-MeBioS, Katholieke Universiteit Leuven, W. de Croylaan 42, B-3001 Leuven, Belgium |
| |
Abstract: | 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 an algorithm to reproduce such correlated non-Gaussian model parameters for use with Monte Carlo simulations. The algorithm works around the problem of non-Gaussian distributions by transforming the observed non-Gaussian probability distributions using a proposed SKN-distribution function before applying the covariance decomposition algorithm to generate Gaussian random co-varying parameter sets. The proposed SKN-distribution function is based on the standard Gaussian distribution function and can exhibit different degrees of both skewness and kurtosis. This technique is demonstrated using a case study on modelling the ripening of tomato fruit evaluating the propagation of biological variation with time. |
| |
Keywords: | 62P10 65C10 |
本文献已被 ScienceDirect 等数据库收录! |
|