Sentence examples for model and conditional from inspiring English sources

Predictions of the non-equilibrium multiple-time-scale mixing model combined with a standard k−ε model and conditional moment closure are also included in the comparison.

More advanced models, such as the G-equation model, BML model, laminar flame-let model, and conditional moment closure (CMC) model are good only for certain flame types (premixed or non-premixed) and flame structures (wrinkle flame and other flames).

Then, we designed the EWMA and CUSUM cause-selecting control charts based on this model and conditional expected value to alleviate censoring mechanism.

The main statistical models for named-entity recognition include hidden Markov model, decision tree model, support vector machine (SVM) model, maximum entropy model, and conditional random fields model.

Workers are assigned to jobs according to some assignment mechanism that we do not model and, conditional on the characteristics of their jobs, they choose how much of their skills to deploy in order to maximize the following utility function: U_{ij}=w_{ij}-1 y_{ij}=w_{ij}-1 y_{ij}) (1).

Common modelling techniques used include random-effects logit or probit model and conditional logit models [ 53, 54], while there is an increasing use of more sophisticated tools such as mixed logit models [ 59].

Comparing the estimates of the coefficient on prefil from the unconditional (model 1) and conditional (model 2) model suggests that observable differences can explain more than a third of the entry earnings advantage of male prefilers.

This happens when the support, which is facilitated by people who have been trained, "involves positive self-disclosure, role modeling and conditional regard," researchers note.

We used methods to account for GPS biases, fractal analyses to determine perceived spatial scale, and discrete choice models and conditional logistic regression to assess resource selection prior to development (i.e., baseline data).

While marginal models, random-effects models, and conditional models are routinely considered to be the three main modeling families for continuous and discrete repeated measures with linear and generalized linear mean structures, respectively, it is less common to consider non-linear models, let alone frame them within the above taxonomy.

While other statistical models such as hidden Markov models and conditional random fields have been proven to be effective to model human motion [36], they are better suited for sequential actions characterizing an activity [37].