Using these procedures, we estimated the smoothest resistivity structure in which the model response fit the data to an RMS tolerance of 1.0.
To evaluate how the model responses fit the synthetic data, we define the root-mean-square (RMS) data misfit as {text{RMS}}_{text{d}} = sqrt {frac{{sumnolimits_{j = 1}^{text{ND}} {left[ {(d_{j}^{text{obs}} - d_{j}^{text{cal}} )/{text{err}}_{j} } right]^{2} } }}{text{ND}}}, (16 where ND is the number of data and err j is the observation error.
The model response is fitted best to the data with a gradient-based optimisation strategy, requiring the sensitivity analysis of the micromechanical model.
The significance of the different terms was determined by the analysis of variance, fitting the model Response = general mean + block + genotype + treatment + genotype.treatment + error; with the term genotype representing the hybrid families (100 × 12 BC1S1 or 100 × 12 BC2S12.
The prediction profile likelihood (9) PPL (z ) = max θ ∈ { θ | F (D pred, θ ) = z } LL (y | θ ) is obtained by maximization over the model parameters satisfying the constraint that the model response F(D θ∗) after fitting is equals to the considered value z for the prediction.
While most participants' responses fit the model, responses of several individuals misfit the model.
The calibration of the effective parameters is achieved by best fitting the model responses of the quantities of interest (e.g., reactor power, average fuel and coolant temperatures) to the actual evolution profiles, here simulated by the Quandry based reactor kinetics (Quark) code available from the Nuclear Energy Agency.
The traditional way of doing this would be to carry out some kind of ad hoc normalization for the count data and then fit the model responses to the normalized data using for example least squares or maximum likelihood methods.
Using a multilinear regression with the 9 PCs as predictor variables and binary responses, it was possible to model the fitted response using a normal distribution for each class (explosives and explosive mimics).
When R 2 approaches unity, the better the response model fits the actual data.
