Based on the inverse modeling results, the seven sorption-process models are discriminated using four model discrimination (or selection) criteria, Akaike information criterion (AIC), modified Akaike information criterion (AICc), Bayesian information criterion (BIC) and Kashyap information criterion (KIC).
The multi-criteria information is extracted from the distribution model and the boundaries of the input image.
The proposed method is based on the estimation of the multi-criteria information of the degraded images.
For the B-criterion, information other than BCF in fish could be considered and more information related to bioaccumulation processes should be gathered (e.g. in species other than fish, different uptake routes).
In our numerical studies, two model selection criteria, Bayesian information criterion (BIC) [ 23] and bias-corrected Akaike information criterion (AICc) [ 24, 25], are considered.
Criteria like an Information Criterion (AIC) and Bayesian information criterion (BIC) are suggested to establish a ranking of models (Akaike, 1973; Schwartz, 1978).
The study analyzes the performance of three model selection criteria, namely, the Akaike information criterion, the Bayesian information criterion, and the Anderson Darling criterion.
The government poses one criterion on information control, that is, whether it undermines its rule.
For model comparison three criteria, namely loglikelihood, Akaike information criterion (AIC) and Bayesian information criterion (BIC) are calculated.
First, we can use some objective criteria such as Bayesian information criterion (BIC).
