Methods for Parameter Ranking in Nonlinear , Mechanistic Models

Abstract

The paper addresses e¢ cient methods for parameter sensitivity analysis and ranking in large, nonlinear, mechanistic models requiring examination of many points in the parameter space. The paper shows how orthogonal decomposition and permutation of the sensitivity derivative is an intuitive and structured method for automatic ranking of the parameters… (More)

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