Analysis of functions on the finite dimensional Euclidean space with respect to the Lebesgue measure is fundamental in mathematics. The extension to infinite dimension is a great challenge due to the lack of Lebesgue measure on infinite dimensional space. Instead the most popular measure used in infinite dimensional space is the Gaussian measure, which has been unified under the terminology of "abstract Wiener space".
Out of the large amount of work on this topic, this book presents some fundamental results plus recent progress. We shall present some results on the Gaussian space itself such as the Brunn–Minkowski inequality, Small ball estimates, large tail estimates. The majority part of this book is devoted to the analysis of nonlinear functions on the Gaussian space. Derivative, Sobolev spaces are introduced, while the famous Poincaré inequality, logarithmic inequality, hypercontractive inequality, Meyer's inequality, Littlewood–Paley–Stein–Meyer theory are given in details.
This book includes some basic material that cannot be found elsewhere that the author believes should be an integral part of the subject. For example, the book includes some interesting and important inequalities, the Littlewood–Paley–Stein–Meyer theory, and the Hörmander theorem. The book also includes some recent progress achieved by the author and collaborators on density convergence, numerical solutions, local times.
Author(s): Yaozhong Hu
Publisher: World Scientific
Year: 2016
Language: English
Pages: 472
Title......Page 1
Copyright......Page 2
Dedications......Page 3
Preface......Page 5
Contents......Page 7
1. Introduction......Page 10
2. Garsia-Rodemich- Rumsey Inequality......Page 15
3. Analysis with Respect to Gaussian Measure in Rd......Page 27
4. Gaussian Measures on Banach Space......Page 75
5. Nonlinear Functionals on Abstract Wiener Space......Page 110
6. Analysis of Nonlinear Wiener Functionals......Page 159
7. Some Inequalities......Page 224
8. Convergence in Density......Page 277
9. Local Time and (Self-) Intersection Local Time......Page 314
10. Stochastic Differential Equation......Page 343
11. Numerical Approximation of Stochastic Differential Eequation......Page 397
Appendix......Page 429
Bibliography......Page 455
Index......Page 469