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9783540289982

Multidimensional Diffusion Processes

by ;
  • ISBN13:

    9783540289982

  • ISBN10:

    3540289984

  • Edition: Reprint
  • Format: Paperback
  • Copyright: 2006-01-15
  • Publisher: Springer Verlag
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Supplemental Materials

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Summary

"This book is an excellent presentation of the application of martingale theory to the theory of Markov processes, especially multidimensional diffusions. This approach was initiated by Stroock and Varadhan in their famous papers. (...) The proofs and techniques are presented in such a way that an adaptation in other contexts can be easily done. (...) The reader must be familiar with standard probability theory and measure theory which are summarized at the beginning of the book. This monograph can be recommended to graduate students and research workers but also to all interested in Markov processes from a more theoretical point of view." Mathematische Operationsforschung und Statistik, 1981

Author Biography

Daniel W. Stroock is Simons Professor of Mathematics at the Massachusetts Institute of Technology in Cambridge, Massachusetts, USA.

Table of Contents

Frequently Used Notation xi
Chapter 0. Introduction 1(6)
Chapter 1. Preliminary Material: Extension Theorems, Martingales, and Compactness 7(39)
1.0 Introduction
7(1)
1.1 Weak Convergence, Conditional Probability Distributions and Extension Theorems
7(12)
1.2 Martingales
19(11)
1.3 The Space C([0, infinity); Rd)
30(6)
1.4 Martingales and Compactness
36(6)
1.5 Exercises
42(4)
Chapter 2. Markov Processes, Regularity of Their Sample Paths, and the Wiener Measure 46(19)
2.1 Regularity of Paths
46(5)
2.2 Markov Processes and Transition Probabilities
51(5)
2.3 Wiener Measure
56(4)
2.4 Exercises
60(5)
Chapter 3. Parabolic Partial Differential Equations 65(17)
3.1 The Maximum Principle
65(6)
3.2 Existence Theorems
71(8)
3.3 Exercises
79(3)
Chapter 4. The Stochastic Calculus of Diffusion Theory 82(40)
4.1 Brownian Motion
82(3)
4.2 Equivalence of Certain Martingales
85(7)
4.3 Itô Processes and Stochastic Integration
92(12)
4.4 Itô's Formula
104(3)
4.5 Itô Processes as Stochastic Integrals
107(4)
4.6 Exercises
111(11)
Chapter 5. Stochastic Differential Equations 122(14)
5.0 Introduction
122(2)
5.1 Existence and Uniqueness
124(7)
5.2 On the Lipschitz Condition
131(1)
5.3 Equivalence of Different Choices of the Square Root
132(2)
5.4 Exercises
134(2)
Chapter 6. The Martingale Formulation 136(35)
6.0 Introduction
136(3)
6.1 Existence
139(6)
6.2 Uniqueness: Markov Property
145(4)
6.3 Uniqueness: Some Examples
149(3)
6.4 Cameron-Martin-Girsanov Formula
152(5)
6.5 Uniqueness: Random Time Change
157(4)
6.6 Uniqueness: Localization
161(4)
6.7 Exercises
165(6)
Chapter 7. Uniqueness 171(24)
7.0 Introduction
171(3)
7.1 Uniqueness: Local Case
174(13)
7.2 Uniqueness: Global Case
187(3)
7.3 Exercises
190(5)
Chapter 8. Itô's Uniqueness and Uniqueness to the Martingale Problem 195(13)
8.0 Introduction
195(1)
8.1 Results of Yamada and Watanabe
195(9)
8.2 More on Itô Uniqueness
204(3)
8.3 Exercises
207(1)
Chapter 9. Some Estimates on the Transition Probability Functions 208(40)
9.0 Introduction
208(1)
9.1 The Inhomogeneous Case
209(24)
9.2 The Homogeneous Case
233(15)
Chapter 10. Explosion 248(13)
10.0 Introduction
248(1)
10.1 Locally Bounded Coefficients
249(5)
10.2 Conditions for Explosion and Non-Explosion
254(5)
10.3 Exercises
259(2)
Chapter 11. Limit Theorems 261(24)
11.0 Introduction
261(1)
11.1 Convergence of Diffusion Process
262(4)
11.2 Convergence of Markov Chains to Diffusions
266(6)
11.3 Convergence of Diffusion Processes: Elliptic Case
272(7)
11.4 Convergence of Transition Probability Densities
279(4)
11.5 Exercises
283(2)
Chapter 12. The Non-Unique Case 285(19)
12.0 Introduction
285(1)
12.1 Existence of Measurable Choices
286(4)
12.2 Markov Selections
290(6)
12.3 Reconstruction of All Solutions
296(6)
12.4 Exercises
302(2)
Appendix 304(24)
A.0 Introduction
304(2)
A.1 Lp Estimates for Some Singular Integral Operators
306(9)
A.2 Proof of the Main Estimate
315(8)
A.3 Exercises
323(5)
Bibliographical Remarks 328(3)
Bibliography 331(6)
Index 337

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