When models employ empirical estimates of each codon frequency (F61 method) the number of model parameters increases by 51 for models with homogenous π's, and by 51 × g for models with heterogeneous π's among partitions.
Furthermore, the number of model parameters increases with the number of hypothetical mechanisms, and model reduction may become infeasible due to the 'curse of dimensionality' (Sunnåker et al., 2013a).
We also observe that as the number of model parameters increases, many inferred migration rates deviate significantly from the true values (for instance, see models 6 and 7 in Table 2).
Although Bruno (1996) [ 14] attempted to account for this phenomenon by modelling amino acid frequencies at each site of the alignment, this approach leads to a serious statistical problem [ 16] whereby the number of model parameters increases linearly with the amount of data.
Based on the results, it is concluded that the liquefaction potential index of the soil increases considerably with an increase in variability of model parameters, increasing the probability of soil liquefaction.
As the number of model parameters increase, classic implementations of optimization methods on one single machine are no longer feasible.
With increase in the number of free parameters, the hardness of estimating model parameters increase, the running time of the learning algorithm also rises and some parameters may overfit due to data scarcity for corresponding features.
In addition, we also estimated the 'goodness' of each model using the Akaike information criterion (AIC), and Bayesian information criterion (BIC) which are penalized model selection criteria that track model performance as the number of model parameters increase [ 27].
Our results show that across a variety of model parameter possibilities, increased investment in post-fire revegetation reduces long-term fire management costs by more than enough to offset the costs of revegetation.
Then it continues until convergence happens by adjusting the probabilities of each model parameter to increase the probability of the model in accordance with the training set being scanned.
