Some formulas for the prediction of the values of a band-limited function based on its samples from the past are generalized by including past samples of its first derivative. The new sums, developed by an approach based on Newton series, make it possible to double the distance between the sample points. The resulting formulas are shown to apply to the prediction problem for a large class of entire functions of exponential type. In addition, a related prediction formula which uses past samples of successively higher derivatives is shown to behave similarly to the Taylor series approximation, again for a class of functions that includes the band-limited functions.

Some basic properties of dyadically stationary (DS) processes are introduced, including continuity and spectral representation. A sampling theorem based on the Walsh functions is investigated for random signals that are not necessarily sequency-limited. By using the concept of a dyadic derivative, the resulting aliasing error is calculated together with the speed of convergence. An example gives a glimpse into the possibilities of applying the sampling theorem as well as the dyadic derivative.

After evaluating Shannon's sampling theorem for functions of several variables, the aliasing error is estimated in dependence upon smoothness conditions. As a main tool the concept of multidimensional convolution integrals is introduced, in particular those of de La Vallée Poussin, which are also helpful in constructing generalized sampling series having better aliasing bounds.