Therefore, regardless of the number of phases selected by different approaches to model selection, we see that the models with PA are preferred over models without.
Model Selection We use the BIC to select the final model from the possible regression models for a gene i.
For model selection, we used the BIC, values of Entropy and Posterior Class Probability, the BLRT as well as theoretical implications.
For model selection we analysed the likelihood for varying numbered of topics in the range (T in {5, 10, 15,ldots, 100, 150, 200,ldots, 500}).
For model selection, we propose a procedure grounded on the calculation of distances defined in a metric space and a minimization procedure to determine the optimal set of representative models.
Using the AIC to guide theory-based model selection, we find that the best models include mostly ordinary businesses, and one type of what we have termed "discretionary business".
Through model selection, we obtain a graphical model that can be used to describe relationships between banks and, specifically, to understand how risks propagate in a systemic risk perspective.
For the model selection, we employed a 2-fold inner cross-validation, again to ensure a test set size of at least 5. Figure 11 summarizes the results of the multi-task approaches compared to the baseline methods.
Based on AIC model selection, we found that inverse power models fit the relationship between human population density and shark sighting frequency better than inverse exponential models.
During model selection, we used the harmonic mean of the MCMC chain to determine the model with the highest marginal likelihood [50].
