
General Stochastic Measures
Integration, Path Properties and Equations
- English
- ePUB (mobile friendly)
- Available on iOS & Android
About this book
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.
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Information
Table of contents
- Cover
- Table of Contents
- Title Page
- Copyright
- Abbreviations and Notations
- Introduction
- 1 Integration with Respect to Stochastic Measures
- 2 Path Properties of Stochastic Measures
- 3 Equations Driven by Stochastic Measures
- 4 Approximation of Solutions of the Equations
- 5 Integration and Evolution Equations in Hilbert Spaces
- 6 Symmetric Integrals
- 7 Averaging Principle
- 8 Solutions to Exercises
- References
- Index
- Wiley End User License Agreement