This book is a concise presentation of the normal distribution on the real line and its counterparts on more abstract spaces, which we shall call the Gaussian distributions. The material is selected towards presenting characteristic properties, or characterizations, of the normal distribution. There are many such properties and there are numerous rel evant works in the literature. In this book special attention is given to characterizations generated by the so called Maxwell's Theorem of statistical mechanics, which is stated in the introduction as Theorem 0.0.1. These characterizations are of interest both intrin sically, and as techniques that are worth being aware of. The book may also serve as a good introduction to diverse analytic methods of probability theory. We use characteristic functions, tail estimates, and occasionally dive into complex analysis. In the book we also show how the characteristic properties can be used to prove important results about the Gaussian processes and the abstract Gaussian vectors. For instance, in Section 5.4 we present Fernique's beautiful proofs of the zero-one law and of the integrability of abstract Gaussian vectors. The central limit theorem is obtained via characterizations in Section 7.3.

Klappentext

The normal distribution and its abstract counterparts are a cornerstone in the theory and practice of probability and statistics. Amongst many remarkable properties that they have are their numerous characterization properties and their manifold connections to other, sometimes unexpected, parts of probability. In this book, the author's aim is to present a readable and wide-ranging account of this subject. The reader will be introduced to important topics such as characteristic functions, conditional expectations, symmetries, and tail integration. What makes this book unique is its numerous sidetracks to explore other related topics including large deviations, strong mixing coefficients, the Wiener process, and probability on abstract spaces. Whilst the author assumes that the reader has a firm grounding in the basics of probability theory, it is intended to be read by graduate students and so there are many problems to help the reader gain a deeper understanding of this fascinating topic.

Inhalt
1 Probability tools.- 1.1 Moments.- 1.2 Lp-spaces.- 1.3 Tail estimates.- 1.4 Conditional expectations.- 1.5 Characteristic functions.- 1.6 Symmetrization.- 1.7 Uniform integrability.- 1.8 The Mellin transform.- 1.9 Problems.- 2 Normal distributions.- 2.1 Univariate normal distributions.- 2.2 Multivariate normal distributions.- 2.3 Analytic characteristic functions.- 2.4 Hermite expansions.- 2.5 Cramer and Marcinkiewicz theorems.- 2.6 Large deviations.- 2.7 Problems.- 3 Equidistributed linear forms.- 3.1 Two-stability.- 3.2 Measures on linear spaces.- 3.3 Linear forms.- 3.4 Exponential analogy.- 3.5 Exponential distributions on lattices.- 3.6 Problems.- 4 Rotation invariant distributions.- 4.1 Spherically symmetric vectors.- 4.2 Rotation invariant absolute moments.- 4.3 Infinite spherically symmetric sequences.- 4.4 Problems.- 5 Independent linear forms.- 5.1 Bernstein's theorem.- 5.2 Gaussian distributions on groups.- 5.3 Independence of linear forms.- 5.4 Strongly Gaussian vectors.- 5.5 Joint distributions.- 5.6 Problems.- 6 Stability and weak stability.- 6.1 Coefficients of dependence.- 6.2 Weak stability.- 6.3 Stability.- 6.4 Problems.- 7 Conditional moments.- 7.1 Finite sequences.- 7.2 Extension of Theorem 7.1.2.- 7.3 Central Limit Theorem.- 7.4 Empirical mean and variance.- 7.5 Infinite sequences and conditional moments.- 7.6 Problems.- 8 Gaussian processes.- 8.1 Construction of the Wiener process.- 8.2 Levy's characterization theorem.- 8.3 Arbitrary trajectories.- 8.4 Second order conditional structure.- A Solutions of selected problems.- A.1 Solutions for Chapter 1.- A.2 Solutions for Chapter 2.- A.3 Solutions for Chapter 3.- A.4 Solutions for Chapter 4.- A.5 Solutions for Chapter 5.- A.6 Solutions for Chapter 6.- A.7 Solutions for Chapter 7.