Mixing is concerned with the analysis of dependence between sigma-fields
defined on the same underlying probability space. It provides an
important tool of analysis for random fields, Markov processes, central
limit theorems as well as being a topic of current research interest in
its own right. The aim of this monograph is to provide a study of
applications of dependence in probability and statistics. It is divided
in two parts, the first covering the definitions and probabilistic
properties of mixing theory. The second part describes mixing properties
of classical processes and random fields as well as providing a detailed
study of linear and Gaussian fields. Consequently, this book will
provide statisticians dealing with problems involving weak dependence
properties with a powerful tool.