Since the term “random ?eld’’ has a variety of different connotations, ranging from agriculture to statistical mechanics, let us start by clarifying that, in this book, a random ?eld is a stochastic process, usually taking values in a Euclidean space, and de?ned over a parameter space of dimensionality at least 1. Consequently, random processes de?ned on countable parameter spaces will not 1 appear here. Indeed, even processes on R will make only rare appearances and, from the point of view of this book, are almost trivial. The parameter spaces we like best are manifolds, although for much of the time we shall require no more than that they be pseudometric spaces. With this clari?cation in hand, the next thing that you should know is that this book will have a sequel dealing primarily with applications. In fact, as we complete this book, we have already started, together with KW (Keith Worsley), on a companion volume [8] tentatively entitled RFG-A, or Random Fields and Geometry: Applications. The current volume—RFG—concentrates on the theory and mathematical background of random ?elds, while RFG-A is intended to do precisely what its title promises. Once the companion volume is published, you will ?nd there not only applications of the theory of this book, but of (smooth) random ?elds in general.
表中的内容
Gaussian Processes.- Gaussian Fields.- Gaussian Inequalities.- Orthogonal Expansions.- Excursion Probabilities.- Stationary Fields.- Geometry.- Integral Geometry.- Differential Geometry.- Piecewise Smooth Manifolds.- Critical Point Theory.- Volume of Tubes.- The Geometry of Random Fields.- Random Fields on Euclidean Spaces.- Random Fields on Manifolds.- Mean Intrinsic Volumes.- Excursion Probabilities for Smooth Fields.- Non-Gaussian Geometry.
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