Model parameter estimation with imprecise information
Wolfgang Rauch,
Nikolaus Rauch,
Manfred Kleidorfer
Abstract:Model parameter estimation is a well-known inverse problem, as long as single-value point data are available as observations of system performance measurement. However, classical statistical methods, such as the minimization of an objective function or maximum likelihood, are no longer straightforward, when measurements are imprecise in nature. Typical examples of the latter include censored data and binary information. Here, we explore Approximate Bayesian Computation as a simple method to perform model param… Show more
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