For the NMF approach, the parameters are set as,,,, and according to [16].
With this approach, few parameters are needed to predict adsorption equilibria.
From a rational approach, key parameters are identified and discussed to identify promising membrane materials.
In this innovative approach, uncertain parameters are mathematically modelled as a set of correlated scenarios with known probability of occurrence.
In the first approach, all parameters are estimated using the full log-likelihood in equation (4).
In the Bayesian approach all parameters are estimated via MCMC sampling methods.
In the variational Bayesian approach, model parameters are estimated as the posterior distribution.
Thus, using classical approach, the parameters are not identifiable so that one has to make some assumptions such as that growth rates (birth rates) of different stage cancer initiated cells are equal which can hardly be realistic in carcinogenesis.
In parametric approaches, explicit parameters are extracted from the respective time-frequency distributions and used as features for classification [10].
Finally, by disturbing the time series data and repeating the inference approach, parameters which are robustly unequal to zero were identified.
