Abstract
<strong>Title:</strong> Automatic differentiation algorithms in model analysis<br/><strong>Author:</strong> M.J. Huiskes<br/><strong>Date:</strong> 19 March, 2002<p>In this thesis automatic differentiation algorithms and derivativebased methods are combined to develop efficient tools for model analysis. Automatic differentiation algorithms comprise a class of algorithms aimed at the derivative computation of functions that are represented as computer code. Derivativebased methods that may be implemented using these algorithms are presented for sensitivity analysis and statistical inference, particularly in the context of nonlinear parameter estimation.</p><p>Local methods of sensitivity analysis are discussed for both explicit and implicit relations between variables. Particular attention is paid to propagation of uncertainty, and to the subsequent uncertainty decomposition of output uncertainty in the various sources of input uncertainty.</p><p>Statistical methods are presented for the computation of accurate inferential information for nonlinear parameter estimation problems by means of higherorder derivatives of the model functions. Methods are also discussed for the assessment of the appropriateness of model structure complexity in relation to quality of data.</p><p>To realize and demonstrate the potential of routines for model analysis based on automatic differentiation a software library is developed: a C++ library for the analysis of nonlinear models that can be represented by differentiable functions in which the methods for parameter estimation, statistical inference, model selection and sensitivity analysis are implemented. Several experiments are performed to assess the performance of the library. The application of the derivativebased methods and the routines of the library is further demonstrated by means of a number of case studies in ecological assessment. In two studies, large parameter estimation procedures for fish stock assessment are analyzed: for the Pacific halibut and North Sea herring species. The derivativebased methods of sensitivity analysis are applied in a study on the contribution of Russian forests to the global carbon cycle.
Original language  English 

Qualification  Doctor of Philosophy 
Awarding Institution 

Supervisors/Advisors 

Award date  19 Mar 2002 
Place of Publication  S.l. 
Publisher  
Print ISBNs  9789058086013 
Publication status  Published  2002 
Keywords
 mathematics
 mathematical models
 differentiation
 algorithms
 computer analysis
 statistical inference
 statistical analysis
 sensitivity
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Huiskes, M. J. (2002). Automatic differentiation algorithms in model analysis. S.n. https://edepot.wur.nl/196517