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Subgrid model comparison showed that the locally dynamic procedure is necessary for complex flow simulation.
Model comparison showed that our method performed better than traditional ones, in both the evaluation and validation processes.
The results of Bayesian model comparison showed that a model with time-varying thresholds whose parameters are updated by a reinforcement learning algorithm is the most likely model.
The result from the model comparison showed that the model-based reinforcement learning approach provided the best account for the data and outperformed heuristics in explaining the behavioral data in the re-planning trials.
Analysis for model comparison showed that the combined-PI predictor added significant information to tumor size and TP53 mutations (analysis of deviance p = 0.04; Akaike's Information Criterion AICC) for model with and without combined-PI = 191.8 and 194.01, respectively; Table S2).
Bayesian model comparison showed that behavior strongly supported active inference, based on surprise minimization, over classical utility maximization schemes.
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The proposed model comparison shows the perspectives of the joint use of both 0D and CFD 3D model for ICE combustion investigations.
Model comparison, showing using human-discovered types with and without distance information, as well as our model incorporating just connectivity, connectivity and distance, or connectivity, distance, and synaptic depth (as well as the alternative latent position cluster model, see text).
The simulations used for the model comparison shown in Figure 7 were carried out using point estimates of the model parameters chosen from the posterior distributions for each of the models.
GWR model comparisons showed that the local kernel bandwidth GWR model was more realistic than the global kernel bandwidth GWR model, as the latter exaggerated local spatial variation.
Model comparisons showed that the area of both natural forests and plantations surrounding the study sites influenced burial and removal rates.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com