The effects of parametrization on inverse problems

نویسندگان

  • Robert Baraldi
  • John Nardini
  • Emma Thorpe
چکیده

We consider two example systems, a logistic-growth population model and a damped spring-mass model. These models are each parameterized in two different ways. In one case (the logistic) the parameters are independent in one formulation and dependent in the other. In the other example (the spring-mass), the parameterizations are each independent. We carry out a series of inverse problems for these examples using ordinary least squares on each model. We use uncertainty quantification methods involving asymptotic theory to investigate for evidence of parameter dependence. We examine the off-diagonal elements of the Fisher Information (covariance) matrix to compare the matrices created by dependent and independent parametrizations. We further compare the use of exact vs. asymptotic confidence ellipsoids. Finally we compare the confidence ellipsoids generated by asymptotic theory to those created using Monte Carlo simulations.

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تاریخ انتشار 2014