Simulation of Stochastic Processes with Given Accuracy and Reliability

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.

• Provides an analysis of the theory of Sub-Gaussian (including Gaussian one) and Square Gaussian random variables and processes
• Contains information on the study of the issue of accuracy and confidence level of constructed models not found in other books on the topic
• Provides methods and tools in measuring accuracy and reliability in functional spaces