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This book investigates key aspects of estimation and control theory for systems modeled by stochastic partial differential equations. The models presented describe many processes in turbulence theory, fluid mechanics, hydrology, astronomy and meteorology.
This monograph focuses on the construction of regression models with linear and non-linear constrain inequalities from the theoretical point of view. In includes analysis of the properties of regression with inequality constrains.
This book is devoted to the study and optimization of spatiotemporal stochastic processes - processes which develop simultaneously in space and time under random influences. The book presents problems and content not considered in other books on controlled Markov processes, especially regarding controlled Markov fields on graphs.
Results concerning uniform law of large numbers, convergence of approximate estimates of extreme points, as well as empirical estimates of functionals with probability 1 and in probability are presented. Audience: Specialists in stochastic optimization and estimations, postgraduate students, and graduate students studying such topics
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