Spline approximation using knot density functions

This paper, resulting from research collaboration with the UK National Physical Laboratory, is the first to present successfully a
simple method for controlling the location parameters in univariate spline approximations. Traditional highly non-linear
approaches are avoided by considering the parameters to be a function of a given density model. We present a number of
density models for a range of data types, such as dominant local variability. This paper delivers to a scientific discipline applying
polynomial spline approximations to recover discrete data to a high level of accuracy a method which avoids the need to
construct complicated mathematical models.