The displacement and electric potential fields in the proposed method are approximated by using B-spline interpolation functions in x and y directions.
When 1<β<30, the minimizer computed by our method is approximate to the MM method both on the error of the function value and on the minimizer X.
The likelihood method is approximate for nonlinear problems as well but more precise and robust than the linearization method since it does not rely on linearization.
The searching method is approximate string matching test by hamming distance, i.e., we can expect the schemes would be inefficient.
Since most applications rarely meet the aforementioned conditions, it is considered that the method is approximate and that it does not allow the reconstruction of the original movement produced in the three-dimensional scene.
Since this evaluation method is approximate, a full evaluation was conducted on simulated data in Section 4.5.
In practice, defining 'a little better' alignment score requires to set a arbitrary threshold on the score difference and therefore this evaluation method is approximate.
All three methods are approximate and suffer from limitations [9 12].
It is well known that expansion-based stochastic methods are approximate schemes, as they are based on a first or, at most, second order series expansion on the basic variable, e.g. displacement.
However, it should be possible to evaluate the likelihood of the sequence data given the model and its parameterization for γ-MYN compared to simpler models and to evaluate the performance of the models with AIC, even if the methods are approximate rather than proper likelihood estimates.
