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# High Dimensional Model Representation Based Partitioning of a Function’s Data Set With Uncertainty in Data Given Points

DOI link for High Dimensional Model Representation Based Partitioning of a Function’s Data Set With Uncertainty in Data Given Points

High Dimensional Model Representation Based Partitioning of a Function’s Data Set With Uncertainty in Data Given Points book

# High Dimensional Model Representation Based Partitioning of a Function’s Data Set With Uncertainty in Data Given Points

DOI link for High Dimensional Model Representation Based Partitioning of a Function’s Data Set With Uncertainty in Data Given Points

High Dimensional Model Representation Based Partitioning of a Function’s Data Set With Uncertainty in Data Given Points book

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## ABSTRACT

This work focuses on Ihc partitioning of a given finite set of data about, a multivariate function at all nodes of a rectangular hypergrid, the position of its each node contains a small uncertainty. These uncertainties are reflected to weight function by Heav-iside functions in univariate factors such that a parameter which is assumed to be quite small in comparison with the nominal value of the position of each node changes them to delta function when it vanishes. We use first two terms of the expansion of each univariate factor in ascending powers of this parameter. The resulting formulae require the values of the partial derivatives of the multivariate function under consideration with respect to all independent variables at all nodes of the abovementioned hypergrid.