Sentence examples for model parameter vector from inspiring English sources

In the first stage, we reconstruct a model parameter vector to describe facies distribution and boundaries by introducing the level set function which better agrees with Gaussian distribution assumption of Bayesian framework.

Control G.   RIC model Parameter vector Variance of, Shape factor of,   Nonlinear RC model Parameter vector Variance of, Shape factor of, Cramer-Rao Lower Bound (CRLB) [11] is defined as (18).

In the sequel, we will assume, such that the LP model parameter vector can be defined as follows: (8).

The goal of AAM fitting is to find the model parameter vector b ̂ c that best fits an object instance shown in a given input image.

Equation (5) can be rewritten as: (7 Once V, U and Λ are known, the optimal model parameter vector m o can easily be calculated for an arbitrary hyper-parameter λ, since Λ is diagonal.

A model parameter vector w∗ can be represented as a linear combination of these basis vectors, satisfying the approximation condition w∗≈Da, where a is the coefficient vector which can be considered as the representation of w∗ over the dictionary D. In order for D to be flexible and robust to noise, we set the dictionary to be overcomplete (k>d).

Particularly, here, taking the model parameter vectors of all the individuals as the high-dimensional vector space, we seek a dictionary to represent these model parameter vectors.

In this phase, we learn the model parameter vectors of all individuals, which lead to the construction of the matrix W∗.

The dictionary will be learned from data, and it helps regularize the learning of the models since it requires the model parameter vectors to be (sparse) linear combination of the dictionary bases.

To fulfill this idea, here, we denote the set of model parameter vectors of all the individuals as (mathbf {W}^{ast }=left [ mathbf {w}_{1}^{ast }ldots,mathbf {w}_{i}^{ast },ldots,mathbf {w}_{N}^{ast }right ]), where (mathbf {w}^_{i}) represents weight coefficient vector of the i t h patient learned from the CHI model.

The parameter "pool" φ is therefore simply a concatenation of all model parameter vectors.