The development of computationally efficient model selection strategies
represents an important problem facing the analysis of Nuclear Fusion
experimental data, in particular in the field of scaling laws for the
extrapolation to future machines, and image processing. In this paper, a new
model selection indicator, named Model Falsification Criterion (MFC), will be
presented and applied to the problem of choosing the most generalizable scaling
laws for the power threshold to access the H-mode of confinement in Tokamaks.
The proposed indicator is based on the properties of the model residuals, their
entropy and an implementation of the data falsification principle. The model
selection ability of the proposed criterion will be demonstrated in comparison
with the most widely used frequentist (Akaike Information Criterion) and
bayesian (Bayesian Information Criterion) indicators.Comment: 4 pages, 2 figure
