Estimating parameter uncertainty in binding-energy models by the frequency-domain bootstrap

Understand

We propose using the frequency-domain bootstrap (FDB) to estimate errors of modeling parameters when the modeling error is itself a major source of uncertainty.

• Unlike the usual bootstrap or the simple $\chi^2$ analysis, the FDB can take into account correlations between errors.
• It is also very fast compared to the the Gaussian process Bayesian estimate as often implemented for computer model calibration.
• The method is illustrated drop model of nuclear binding energies.