Sentence examples for transform the sample from inspiring English sources

A six-step framework is proposed to perform the joint simulation using MAF and SGS: Step 1: Primary Gaussian transformation: Running Gaussian anamorphosis to transform the sample data (Zleft( x right)) into normal (Gaussian) scores (Yleft( x right)); Yleft( x right) = phi left( {Zleft( x right)} right) (1).

One way to overcome this problem is to transform the sample data in some way so that some sample characteristics are evident from the histogram plot.

Thus, Z can be computed as Z = B −1 Y. Generally, when correlation exists among input variables, it is advisable to sample in the independent standard normal space, and then transform the sample points into the original state space.

Weights were used to transform the sample back to the original distribution of positive and negative algorithms [ 34].

Moreover, by employing the input-delay approach, we transform the sampling data into time-varying delayed data and on the basis of receiving data, two sampled-data control models are proposed.

The key idea of KPCA is to define a nonlinear transformation ϕ which transforms the sample data into a high-dimensional data space, where each data point X i is projected to a point ϕ(X i ).

From the perspective of ELM, h(x i ) is a feature map which transforms the sample x i into ELM feature space.

However, it was found also that if the target-auxiliary variable relationship for any sample did not pass through the origin, it could easily be made to do so by fitting a straight line regression to the data and then transforming the sample data by subtracting the regression constant from all the target variable values in the sample.

In order to reduce fieldwork costs, the sample was clustered by neighbourhood code and a new selection was done proportional to the size of the target population in the cluster, which transformed the sample into a clustered random one still allowing the calculation of selection probabilities.

Next, we use the invertible function T α to transform the compressed samples.

With such a nonlinear transform, the original samples are linearly divided into arbitrary clusters without increasing the computational complex.