Sentence examples for model formulation to from inspiring English sources

We extend mixture model formulation to cooperative spectrum occupancy prediction using decision (hard) and data (soft) fusion techniques.

In this paper, we present an elucidating model formulation to account for customer segmentation within a mixed-integer program that enables to consider customer choice behavior by an MNL that accounts for customer characteristics (Sect. 3.1).

This specific example, (84), is relevant for the reduction of the strongly non-local model formulation to the weakly non-local case in the "Special case: weakly non-local model formulation" section.

We choose a parametric Weibull hazard model formulation to account for the baseline trend of the upselling likelihood during the customer lifetime and to allow for a time-varying formulation of our focal variables, pre- and post-purchase interactions, service requests, and service complaints (Grewal et al. 2004; Kamakura et al. 2004).

Another important advantage of these models is that they include a regularization (or penalization) parameter in their standard model formulation to avoid overfitting, a procedure comparable to shrinkage methods for logistic regression models [ 15].

The model is nitrogen-based and the design is deliberately general enough and modular to enable many of the existing ecosystem model formulations to be simulated and hence model-to-model comparisons rendered feasible.

In this study, we applied a pore network modeling formulation to simulate the velocity field driven by pressure and electric potential to calculate and compare the corresponding average solute dispersivity values.

Use of these two exposure response relations in this risk assessment illustrates that the adequacy of the model formulation relative to the study population appears to be at least as important as the quality of adjustment of the model to the epidemiologic data.

We propose that a multi-model approach should be preferred and a suite of possible predictions considered if biases due to uncertainty in data and model formulation are to be minimised.

During numeric model formulation, used to identify the muscle forces of the mastication organ in the biocybernetic perspective, the following assumptions were adopted.

The prediction region of the model with the cloglog link contains all observed data points, while not extending far beyond the studies with lowest observed Se and Sp. This is reflected in a lower deviance information criterion (DIC), a measure of model fit, for the cloglog model formulation compared to the logit formulation.