Sentence examples for better than simpler models from inspiring English sources

The authors found that models inputted with infrastructure, vegetation management, ProxPix, performed up to 5 13% better than simpler models depending on model evaluation criteria and input data; and that the model forced with utility infrastructure and ProxPix had the best overall performance.

The cost-of-control model fitted the data better than simpler models in which only the force or precision were allowed to vary with reward (ΔAIC = 6.5; Table S3; "Model Comparison" in Supplemental Experimental Procedures).

By allowing each participant to optimize behavior according to their own noise and temporal discounting, the model was able to accommodate individual differences in responses to reward across the populations, better than simpler models.

The site-heterogeneous CAT model has been repeatedly shown to fit real data better than simpler models such as the site-homogenous model used in the ML analyses, and to be better able to overcome systematic error [ 11].

It has been reported that covarion models fit protein sequence comparison data better than simpler models of rate variation in which site constraints remain unchanged throughout time [ 37, 38].

We found that models A (test 1) and B only fitted the data significantly better than simpler models when human RPS4Y2 lineage was used as the foreground branch (Table 2 for model A); this indicates that positive selection acted on some sites of the human RPS4Y2 gene.

It has been well established that appropriately partitioned models of sequence evolution yield a better fit to empirical datasets than simple models [ 47- 49] and simulation experiments have shown that phylogenetic analysis with suitably partitioned models results in more accurate trees [ 50].

The results suggest that correlative models can perform as well as or better than simple mechanistic models.

The alternative methods had better selection probabilities in compound models than simple models; it appears that Akaike information criterion or F test selection is more likely to select compound hypotheses over simple ones, regardless of the true underlying model.

A possible explanation why the distributed model did not perform better than the simpler model structures is the use of limited available spatial information.

A parameter rich model naturally fits the data better than a simpler model [47].