Sentence examples for multivariate function that from inspiring English sources

This paper presents a new general neural structure based on nonlinear flexible multivariate function that can be viewed in the framework of the generalised regularisation networks theory.

However, theoretically, when the number of accelerated stresses is greater than one, the stress-life relationship changes into a binary or multivariate function that leads to problems that are different from those of planning single stress ALTs.

Table 2 shows comparisons of the coefficients of the three multivariate predictive functions that were estimated using the NHS-92 cohort with the published coefficients from the SAHS, ARIC and Framingham models.

If we consider signaling networks as multivariate nonlinear functions that map various perturbations and extracellular cues onto different cellular responses it becomes clear that only by understanding the network dynamics and interplay between its key components as a whole will we be able to construct predictive and descriptive models of cell decision processes.

The emphasis in such studies will be on developing a gene expression-based multivariate function, or predictor, that accurately predicts the class membership of a new sample on the basis of the expression levels of key genes.

Based on the Sklar theorem, for a given joint multivariate PDF and the relevant marginal PDFs, there exists a copula function that relates them.

The foundation theorem for copula was introduced by Sklar which states that for a given joint multivariate PDF and the relevant marginal PDFs, there exists a copula function that relates them.

The statistic of the joint acquisition is the multivariate function, so it is assumed that the receiver positions only in the X axis and Y axis is unknown to show the three-dimensional function.

It is known that a multivariate function f can be well approximated by the linear combination of the elements of a given basis: f = ∑ k α k γ k (1).

A natural extension of the previous approach for multivariate data is to extract the intersection among inverse images of multiple function values first, and then track the topological changes inherent in that intersection with respect to the multivariate function value changes.

The method involves novel decomposition at the most probable point that facilitates a univariate approximation of a general multivariate function, response surface generation of the univariate function, and Monte Carlo simulation.