Sentence examples for model f g from inspiring English sources

We therefore need a score s g (t) for each candidate transcription factor t ∈ T g to assess how likely it is to be involved in the regression model f g.

The off-road motion model f g ( x t g, η t g ) in (2) is selected to be the following constant velocity model with the state vector x g = (x g y g z g v g ψ T, where x g,y g,z g is the 3D location in a global Cartesian reference system, v g is the translational speed in the x g y g -plane, and ψ is the course.

Here, according to the proposed measurement error model, f g is a multivariate Gaussian distribution with a diagonal variance-covariance matrix.

More general models are possible, for example [ 20] model f g with a random forest [ 29] and score a predictor s g (t) with a variable importance measure specific to this model.

Applying the model f g, we map the new protein gnew onto the pharmacological feature space as (8) where w g j is a weight vector and s g is a sequence similarity score.

For example, if we model f g as a linear function (2) f g (X T g ) = ∑ t ∈ T g β t, g X t, then the score s g (t) should typically assess the probability that β t, g is non-zero [ 23].

We use kinetic models f G based on Michaelis Menten functionals (Leskovac, 2003).

The problem is formulated as follows: Given the model structure (f, g), which is assumed to be affine in the input, and the specific parameter of interest, find a feedback law that maximizes the sensitivity of the model output to the parameter under different flow conditions in the water tank.

The prediction models with both clinical and gene expression variables (Models E, F, G, and H) show little or no improvement over the clinical models.

For 2010.0, three models (B, F, G) have smaller data residuals than other models.

For epoch 2005.0, one model (D) is abnormally far from the testing dataset, while four models (A, B, F, G) have the smallest data residuals.