  A method based on orthogonal function series interpolation of the square root
probability density to analyze higher dimensional scattered data is presented.
The method is targeted for the use-case when the model and/or data are
available only as discrete events. While fast and efficient algorithms are well
known for pseudo-spectral (grid node based) methods, this work focuses on a
spectral (non grid based) approach. A typical application is the extraction of
physics model parameters from events detected in high energy particle
collisions. Several examples are provided and the performance is compared to
existing conventional procedures. In some cases the method can be shown to
behave as an optimal observable of the data, exemplified by the ability to
approach the Cramer-Rao bound.
