A stochastic framework for recursive computation of spline functions--Part I: Interpolating splines

The method for exploiting stochastic smoothing techniques to develop dynamical recursive algorithms for the deterministic problem of d interpolation (optimal curve fitting) is shown. A reproducing kernel Hilbert space approach is used to develop an explicit correspondence between spline interpolation and linear least-squares smoothing of a particular zero-mean random process. This random process is shown to be the output of a white-noise-driven dynamical system whose parameters and initial conditions are fixed by the functional form chosen for the spline. A recursive algorithm is then derived for this (nonstandard) smoothing problem, and thus also for the original spline interpolation problem.