Vadym M. Radchenko

General Stochastic Measures (eBook, PDF)

Integration, Path Properties and Equations

• Format: PDF

Produktbeschreibung

This book is devoted to the study of stochastic measures (SMs). An SM is a sigma-additive in probability random function, defined on a sigma-algebra of sets. SMs can be generated by the increments of random processes from many important classes such as square-integrable martingales and fractional Brownian motion, as well as alpha-stable processes. SMs include many well-known stochastic integrators as partial cases. General Stochastic Measures provides a comprehensive theoretical overview of SMs, including the basic properties of the integrals of real functions with respect to SMs. A number of results concerning the Besov regularity of SMs are presented, along with equations driven by SMs, types of solution approximation and the averaging principle. Integrals in the Hilbert space and symmetric integrals of random functions are also addressed. The results from this book are applicable to a wide range of stochastic processes, making it a useful reference text for researchers and postgraduate or postdoctoral students who specialize in stochastic analysis.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: For Dummies
• Seitenzahl: 272
• Erscheinungstermin: 11. August 2022
• Englisch
• ISBN-13: 9781394163915
• Artikelnr.: 65464335

Autorenporträt

Vadym M. Radchenko is Full Professor in the Department of Mathematical Analysis at Taras Shevchenko National University of Kyiv, Ukraine. His research interests include stochastic integration and stochastic partial differential equations.

Inhaltsangabe

Abbreviations and Notations ix

Introduction xi

Chapter 1 Integration with Respect to Stochastic Measures 1

1.1. Preliminaries 1

1.2. Stochastic measures 2

1.2.1. Definition and examples of SMs 2

1.2.2. Convergence defined by an SM 5

1.3. Integration of deterministic functions 6

1.4. Limit theorems for integral of deterministic functions 11

1.4.1 Convergence of integral A fn dmu 13

1.4.2 Convergence of integral X fdmun 14

1.5. sigma-finite stochastic measures 16

1.6. Riemann integral of a random function w.r.t. a deterministic measure 21

1.6.1. Definition of the integral 21

1.6.2. Interchange of the order of integration 27

1.6.3. Iterated integral and integration by parts 29

1.7. Exercises 32

1.8. Bibliographical notes 34

Chapter 2 Path Properties of Stochastic Measures 35

2.1. Sample functions of stochastic measures and Besov spaces 35

2.1.1. Besov spaces 35

2.1.2. Auxiliary lemmas 37

2.1.3 Stochastic measures on [0, 1] 42

2.1.4 Stochastic measures on [0, 1] d 44

2.2. Fourier series expansion of stochastic measures 46

2.2.1 Convergence of Fourier series of the process mu(t) 46

2.2.2. Convergence of stochastic integrals 49

2.3. Continuity of the integral 51

2.3.1. Estimate of an integral 51

2.3.2. Parameter dependent integral 54

2.3.3. Continuity with respect to the upper limit 55

2.4. Exercises 57

2.5. Bibliographical notes 59

Chapter 3 Equations Driven by Stochastic Measures 61

3.1 Parabolic equation in R (case dmu¯sigma (x)) 61

3.1.1. Problem and the main result 61

3.1.2 Lemma About the Hölder Continuity in X 70

3.1.3 Lemma about the Hölder continuity in t 75

3.2 Heat equation in R¯d (case dmu(t)) 78

3.2.1. Additional estimate of an integral 78

3.2.2. Problem and the main result 80

3.2.3 Lemma About the Hölder Continuity in X 84

3.2.4 Lemma about the Hölder continuity in t 91

3.3 Wave equation in R (case dmu(x)) 99

3.3.1. Problem and the main result 99

3.3.2 Lemma About the Hölder Continuity in X 102

3.3.3 Lemma about the Hölder continuity in t 106

3.4 Wave equation in R (case dmu(t)) 108

3.4.1. Problem and the main result 108

3.4.2 Lemma About the Lipschiz Continuity in X 109

3.4.3 Lemma about the Hölder continuity in t 111

3.5 Parabolic evolution equation in R d (weak solution, case dmu(t)) 114

3.6. Exercises 119

3.7. Bibliographical notes 120

Chapter 4 Approximation of Solutions of the Equations 123

4.1 Parabolic equation in R (case dmu(x)) 123

4.1.1. Problem and the main result 123

4.1.2. Auxiliary lemmas 130

4.1.3. Examples 133

4.2 Heat equation in R¯d (case dmu(t)) 135

4.2.1. Problem and the main result 135

4.2.2. Auxiliary lemma 138

4.2.3. Examples 139

4.3 Wave equation in R (case dmu(t)) 140

4.3.1. Approximation by using the convergence of paths of SMs 140

4.3.2. Approximation by using the Fourier partial sums 142

4.3.3. Approximation by using the Fejèr sums 149

4.3.4. Auxiliary lemma 151

4.3.5. Example 153

4.4. Exercises 154

4.5. Bibliographical notes 155

Chapter 5. Integration and Evolution Equations in Hilbert Spaces 157

5.1. Preliminaries 157

5.2. Equations and integral with a real-