Sentence examples for the sum model from inspiring English sources

In the SUM model, the database structure nodes are denoted by d1, d2,..., d l where l is the number of molecules in the database.

The sum models 'or' behavior of the different TFCs because all TFCs need not be active simultaneously.

From the five models identified (Table 1), the summed model weights for each factor with respect to crop-raiding were habitat type (100%), elevation (97%) and distance to nearest village (29%).

In contrast, the summed model weight of all one-cluster models for the upper Eocene sample was 0, with the remainder distributed among two- (0.894), three- (0.106) and four-cluster models (0.002).

In order to estimate the predicted BOLD response with each condition, we simulated the summed model activity both with and without a decay phase in order to ensure that this did not unjustly influence the comparison between model predictions.

Three methods have previously been presented in Computer and Industrial Engineering for the selection of a statistical distribution to describe a data-set: the weighted sum model, the weighted multiplication model and data envelopment analysis.

The paper uses the 'weighted sum model' technique in multi-criteria decision analysis (MCDA) and the 'additive utility model' in analytical hierarchical process (AHP) for multi-criteria decision making, to develop the model from first principles.

On the basis of the bond valence sum model, the bond-valence parameters d0 of the stronger O H and weaker H⋯O bonds are respectively calculated from the bond length data of hydrogen bonds in hydrated borates, which are available in the Inorganic Crystal Structure Database (2004).

The weighted sum model was selected as the main optimization method.

SMI attribute scores are calculated using the weighted sum model of relevant measures.

k = number of parameter estimates, RSS = residual sum of squares, AICc = Akaike information criterion adjusted for the sample size, w = conditional model probability (likelihood of model i divided by the sum of model likelihoods).