Simulation of Stochastic Processes with Given Accuracy and Reliability
Publication Date: December 2016 Hardback 346 pp.
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.
About the Authors
Yuriy Kozachenko is Professor at Taras Shevchenko National University of Kyiv, Ukraine.
Oleksandr Pogorilyak is Associate Professor at the Department of Probability Theory and Mathematical Analysis, Uzhgorod National University, Ukraine.
Iryna Rozora is Associate Professor at the Department of Applied Statistics, Faculty of Cybernetics, Taras Shevchenko National University of Kyiv, Ukraine.
Antonina Tegza is Associate Professor at the Department of Probability Theory and Mathematical Analysis, Uzhgorod National University, Ukraine.