ABSTRACT
The Gaussian process is by far the most widely studied stochastic model and has found many applications in both physical and biological sciences and in engineering. In the context of signal processing it is impossible to discuss Gaussian noise without mentioning the seminal papers of Rice [1], but his treatment of the subject added to an existing literature going back many decades. Papers of particular relevance to the later chapters of the present book date back to the beginning of the twentieth century and relate to certain population migration problems [2-4]. As a consequence of its long history, excellent accounts of this process are given in most standard textbooks on statistics and stochastic noise such as references 1-3 of Chapter 1. Here a summary will be presented of those features of the Gaussian process that are required to place in context the non-Gaussian models discussed later and to establish the range of their applicability to problems of practical interest.
