TL;DRAbstract
Much of the mathematical literature on parameter estimation can be divided into “theoretical” and “numerical” work. While the predominant goal of the theoretical literature is to develop concepts of uniqueness of solutions in parameter estimation problems (see e.g., [C]) and continuous dependence of the “identified” parameter on the problem data, the numerical analysts are primarily concerned with developing efficient algorithms, or, in some cases where the model equations are rather difficult, with obtaining algorithms which work at all; for surveys we refer to [AE, Kub, P, PG Ra]. Within the numerical work, most to date is centered around the output least squares formulation of parameter estimation problems.
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Much of the mathematical literature on parameter estimation can be divided into “theoretical” and “numerical” work. While the predominant goal of the theoretical literature is to develop concepts of uniqueness of solutions in parameter estimation problems (see e.g., [C]) and continuous dependence of the “identified” parameter on the problem data, the numerical analysts are primarily concerned with developing efficient algorithms, or, in some cases where the model equations are rather difficult, with obtaining algorithms which work at all; for surveys we refer to [AE, Kub, P, PG Ra]. Within the numerical work, most to date is centered around the output least squares formulation of parameter estimation problems.
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