In selecting the better model, the Akaike Information Criterion (AIC, Akaike, 1977; Sakamoto et al., 1983) is widely used, which is defined in our case as where K is the number of free model parameters and equals one for the stationary Poisson model and two for the BPT model.
Model selection With P equal to the number of freely estimated model parameters, N equal to the number of steady state data points, and SSE equal to the sum of squared errors of the fitted model, the Akaike Information Criterion [ 16] used here has the form AIC = 2 P + N log(SSE/N) + 2 P (P + 1 ) N − P − 1 [ 17].
For all calculations, we set initial values of model parameters (s) equal to zero.
where σ 2 denotes thermal noise variance, the second term models the receiver impairment characteristics and is a monotonically increasing function of received signal, defined as nu(x)=frac{k_{3}}{100}x quad left[sqrt{mathbf{mW}}right] (6). in which k 3 is a model parameter and equals EVM %.
In this case, the model parameters are set equal to median values.
The whole organ model uses material parameters equal to our previous mouse models (13), as changes in inflation from PV were consistent with these parameters.
According to the WINNER II models, this parameter equals 18.7 for LoS and 20 for NLoS (see p. 44 in[144).
In linear models, the parameter equals 1.0.
All 6 of these models fit all 5 datasets well (Fig. 7), as one might expect since h not far from 1 implies that the data are not far from the Michaelis-Menten model that lives within each of these models if two parameters equal each, i.e. it is reasonable to expect that each model can adjust its 3rd parameter to meet differences between h = 1 and h = 1.1 to 1.3.
The mean log Bayes Factor for this model, which contains a mere 12 parameters, equals 137.8 (annealing: [127.1; 141.0], melting: [137.0; 146.2]), a significant improvement in terms of model fit compared to the independent model.
