# Sampling Theorems for Stochastic Signals. Appraisal of Paul L. Butzer’s Work

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## Abstract

**:**

## 1. Introduction

## 2. Paul Butzer’s Stochastic Sampling Theoretical Research

- [A] Butzer, P.L. A survey of the Whittaker-Shannon sampling theorem and some of its extensions. J. Math. Res. Exposition
**1983**, 3, 185–212. - [B] Butzer, P.L., Splettstösser, W. Stens, R.L. The sampling theorem and linear prediction in signal analysis. Jahresber. Deutsch. Math.-Verein.
**1988**, 90, 1–70.

- [C] Butzer, P.L., Gather U. Asymptotic expansions for central limit theorems for general linear stochastic processes. I. General theorems on rates of convergence. Math. Methods Appl. Sci.
**1979**, 1, 241–264. - [D] Butzer, P.L., Gather U. Asymptotic expansions for central limit theorems for general linear stochastic processes. II. Models of the general random noise and pulse train processes. Math. Methods Appl. Sci.
**1979**, 1, 346–353.

- [E] Butzer, P.L., Stens R.L. Prediction of nonbandlimited signals from past samples in terms of splines of low degree. Math. Nachr.
**1987**, 132, 115–130.

## 3. The Results

**boldface**). Let $f\in \mathrm{C}(\mathbb{R})$. The

**modulus of continuity**with respect to the difference order r is defined by

**stochastic process**$\{X(t,\omega )\equiv X(t):t\in \mathbb{R}\}$ defined on a standard probability space $(\Omega ,\mathcal{A},\mathsf{P})$ is an $\mathcal{A}$–measurable function, that is $X({t}_{0},\omega )$ becomes a random variable for any fixed $t={t}_{0}\in \mathbb{R}$, while $X(t,{\omega}_{0}),{\omega}_{0}\in \Omega $ is a deterministic function called trajectory of X. The process $X(t)$ belongs to the class of all finite second moment random variables ${\mathrm{L}}^{2}(\Omega ):={\mathrm{L}}^{2}(\Omega ,\mathcal{A},\mathsf{P})$. This class possesses a Hilbert space structure with scalar product defined by the expectation operator $\mathsf{E}$, that is $\langle X,Y\rangle =\mathsf{E}X{Y}^{\ast}$ and with the norm ${\left(\right)open="\parallel "\; close="\parallel ">X}_{}{\mathrm{L}}^{2}$ equipped.

**Karhunen–Cramér theorem**, which characterization theorem can also be called generalized harmonizability theorem [25,26,27], reads as follows [5] (p. 156). Assume that the correlation function ${R}_{X}(t,s)$ of a stochastic process $X(t)$ possesses a double integral representation in the form

**modulus of continuity for stochastic signal X**is defined by

**convolution of a process**$X\in {\mathrm{L}}^{2}(\Omega )$ with a (deterministic) function $g\in {\mathrm{L}}^{1}(\mathbb{R})$ that turns out to be the (stochastic) integral

**Theorem**

**1**([B] (p. 57, Theorem 6.11.))

**.**

**Theorem**

**2**([B] (p. 57, Theorem 6.12.))

**.**

**generalized sampling expansion**[B] (pp. 24–25) when the sinc kernel is replaced with another suitable kernel $\phi $:

**(a)**- there holds true $f(t)={lim}_{w\to \infty}({S}_{w}^{\phi}f)(t)$, for all $f\in \mathrm{C}(\mathbb{R})$, when $t\in \mathbb{R}$;
**(b)**- ${\left\{{S}_{w}^{\phi}\right\}}_{w>0}$ defines a family of bounded linear operators from $\mathrm{C}(\mathbb{R})$ into itself, so that$${\left(\right)open="\parallel "\; close="\parallel ">{S}_{w}^{\phi}f}_{}[\mathrm{C},\mathrm{C}]=0.$$

**Theorem**

**3**([B] (p. 58, Theorem 6.14))

**.**

**Theorem**

**4**([B] (p. 59, Theorem 6.16))

**.**

**(c)**- for r–fold differentiable WSS process X, we have$${\left(\right)open="\parallel "\; close="\parallel ">X(t)-({S}_{w}^{\phi}X)(t)}_{}^{}{\mathrm{L}}^{2}2$$
**(d)**- moreover, there exists an absolute constant $K>0$, such that for WSS process $X\in {\mathrm{L}}^{2}(\Omega )$ there holds$${\left(\right)open="\parallel "\; close="\parallel ">X(t)-({S}_{w}^{\phi}X)(t)}_{}{\mathrm{L}}^{2}$$

**(d)**in Theorem 4, gives an excellent insight into analysis background of probabilistic setting of the aliasing approximation error’s magnitude in function of the bandwidth $w>0$ of the input stochastic process by its generalized sampling expansion series.

## 4. Discussion

**(ii)**studying the resulting truncated series; now, of the correlation function, the authors describe the final behavior of this double sum. Actually, Butzer and co-authors follow the similar derivation steps as the great probabilists Kari Karhunen and Harald Cramér when they establish the above mentioned, so-called Karhunen–Cramér theorem. Karhunen’s and Cramér’s proving method was based on the isometric isomorphy between the so-called Hilbert space of the stochastic process $X(t)$, $\mathcal{H}(X):={\mathcal{L}}^{2}\overline{\{X(t):t\in \mathbb{R}\}}$, say, which is the in medio closure of the linear span of the set $\{X(t):t\in \mathbb{R}\}$, and the H–space

## Funding

## Acknowledgments

**Harmonic Analysis and Applications - Strobl’18**conference in Strobl, Austria, held June 4-8, 2018, to invent and organize a special section

**’Sampling Theory after Paul Butzer’ to honor the 90th birthday of Paul Leo Butzer**. Thanking to this initiative the author has the excellent opportunity to expose his homage talk entitled On generalized derivative sampling series expansion in which several parts of this work were presented. In addition, the author is indebted to all three anonymous referees for their careful reading, constructive suggestions and reorganization requests of the first draft of the manuscript. By their kind guiding process, this survey note encompasses its final desired form.

## Conflicts of Interest

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Pogány, T.K.
Sampling Theorems for Stochastic Signals. Appraisal of Paul L. Butzer’s Work. *Axioms* **2019**, *8*, 91.
https://doi.org/10.3390/axioms8030091

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Pogány TK.
Sampling Theorems for Stochastic Signals. Appraisal of Paul L. Butzer’s Work. *Axioms*. 2019; 8(3):91.
https://doi.org/10.3390/axioms8030091

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2019. "Sampling Theorems for Stochastic Signals. Appraisal of Paul L. Butzer’s Work" *Axioms* 8, no. 3: 91.
https://doi.org/10.3390/axioms8030091