Approximation of random fields by generalized linear splines

We consider the problem of reconstructing stochastic processes or stochastic fields from their known values on a finite grid. This problem is stated and solved in a sufficiently general setting; it is shown that even in the simplest case of approximating a stochastic process by generalized linear splines, the tail of the distribution of the approximation error normalized in an appropriate way decreases exponentially.