Simulation of Stochastic Processes with Given Accuracy and Reliability

Outlines new approaches and modern methods of simulation of stochastic processes with given accuracy and reliability in functional spaces

Yuriy V. Kozachenko (Author), Oleksandr O. Pogorilyak (Author), Iryna V. Rozora (Author), Antonina M. Tegza (Author)

9781785482175, Elsevier Science

Hardback, published 24 November 2016

346 pages
22.9 x 15.1 x 2.5 cm, 0.49 kg

"The book will be useful both for mathematicians and practitioners who deal with stochastic models. It contains rigorous formulas together with simulation results. The mathematical level of the book is high, however it is accessible for everybody who is interested in approximations of stochastic processes." --Zentralblatt MATH 1376

Simulation has now become an integral part of research and development across many fields of study. Despite the large amounts of literature in the field of simulation and modeling, one recurring problem is the issue of accuracy and confidence level of constructed models. By outlining the new approaches and modern methods of simulation of stochastic processes, this book provides methods and tools in measuring accuracy and reliability in functional spaces. The authors explore analysis of the theory of Sub-Gaussian (including Gaussian one) and Square Gaussian random variables and processes and Cox processes.  Methods of simulation of stochastic processes and fields with given accuracy and reliability in some Banach spaces are also considered.

1. The Distribution of the Estimates for the Norm of Sub-Gaussian Stochastic Processes.
2. Simulation of Stochastic Processes Presented in the Form of Series.
3. Simulation of Gaussian Stochastic Processes with Respect to Output Processes of the System.
4. The Construction of the Model of Gaussian Stationary Processes.
5. The Modeling of Gaussian Stationary Random?Processes with a Certain Accuracy and Reliability.
6. Simulation of Cox Random Processes.
7. On the Modeling of Gaussian Stationary Processes with Absolutely Continuous Spectrum.
8. Simulation of Gaussian Isotropic Random Fields on a Sphere.

Subject Areas: Probability & statistics [PBT]