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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/4465 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/4465 http://eprints.org/relation/isCoversheetVersionOf https://pure.iiasa.ac.at/id/document/4465 archive 351 disk0/00/00/55/68 2016-01-15 02:10:10 2021-08-27 17:16:24 2016-01-15 02:10:10 monograph show 2 Huiskes M. 7678 Automatic Differentiation and Uncertainty Analysis pub iis_met iis_mod prog_dyn The paper aims to give an overview of the possibilities for using automatic differentiation for uncertainty analysis. It presents an introduction to the general theory of automatic differentiation. Following this an overview of sensitivity analysis and nonlinear regression is given to provide the reader with a clear understanding of both general concepts and their relation to automatic differentiation. Special attention is paid to the effect of nonlinearity on the quality of the obtained estimates and it is investigated how automatic differentiation can be used to improve the estimates. Further the new concept of standard error sensitivity is introduced and formulas for efficient computation are derived. Finally the Oak system is discussed. This system is an implementation of the theory discussed in this paper using the ADOL-C library for automatic differentiation. To demonstrate the possibilities of this system several models used at the IIASA Sustainable Boreal Forests Project have been investigated. 1998-08 published IR-98-083 IR-98-083 1334 public interim_report IIASA, Laxenburg, Austria 48 FALSE info:eu-repo/semantics/book