Pavel S. Knopov - Böcker

This book is devoted to the study and optimization of spatiotem- ral stochastic processes. These are processes that simultaneously - velop in space and time under random influences. Such processes - cur almost everywhere when the global behavior of complex systems is studied, e.g., in physical and technical systems, population dynamics, neural networks, computer and telecommunication networks, complex production networks and flexible manufacturing systems, logistic n- works and transportation systems, environmental engineering, climate modeling and prediction, earth surface models, and so on. In the study of spatiotemporal stochastic processes the classical c- cepts of random fields (which are models for spatially distributed r- dom phenomena) and of stochastic processes (hich are usually thought to describe the evolution over time of systems under random influences) converge. Over the last twenty years,many research monographs were written with emphasis on this unifying point of view, as were a huge number of articles and papers on this subject.

This book contains problems of stochastic optimization and identification. 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

This book contains problems of stochastic optimization and identification. 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

This monograph focuses on the construction of regression models with linear and non-linear constrain inequalities from the theoretical point of view. Unlike previous publications, this volume analyses the properties of regression with inequality constrains, investigating the flexibility of inequality constrains and their ability to adapt in the presence of additional a priori informationThe implementation of inequality constrains improves the accuracy of models, and decreases the likelihood of errors. Based on the obtained theoretical results, a computational technique for estimation and prognostication problems is suggested. This approach lends itself to numerous applications in various practical problems, several of which are discussed in detailThe book is useful resource for graduate students, PhD students, as well as for researchers who specialize in applied statistics and optimization. This book may also be useful to specialists in other branches of applied mathematics, technology, econometrics and finance

This monograph focuses on the construction of regression models with linear and non-linear constrain inequalities from the theoretical point of view. Unlike previous publications, this volume analyses the properties of regression with inequality constrains, investigating the flexibility of inequality constrains and their ability to adapt in the presence of additional a priori informationThe implementation of inequality constrains improves the accuracy of models, and decreases the likelihood of errors. Based on the obtained theoretical results, a computational technique for estimation and prognostication problems is suggested. This approach lends itself to numerous applications in various practical problems, several of which are discussed in detailThe book is useful resource for graduate students, PhD students, as well as for researchers who specialize in applied statistics and optimization. This book may also be useful to specialists in other branches of applied mathematics, technology, econometrics and finance

Estimation and Control Problems for Stochastic Partial Differential Equations

This book is devoted to the study and optimization of spatiotem- ral stochastic processes. These are processes that simultaneously - velop in space and time under random influences. Such processes - cur almost everywhere when the global behavior of complex systems is studied, e.g., in physical and technical systems, population dynamics, neural networks, computer and telecommunication networks, complex production networks and flexible manufacturing systems, logistic n- works and transportation systems, environmental engineering, climate modeling and prediction, earth surface models, and so on. In the study of spatiotemporal stochastic processes the classical c- cepts of random fields (which are models for spatially distributed r- dom phenomena) and of stochastic processes (hich are usually thought to describe the evolution over time of systems under random influences) converge. Over the last twenty years,many research monographs were written with emphasis on this unifying point of view, as were a huge number of articles and papers on this subject.

Focusing on research surrounding aspects of insufficiently studied problems of estimation and optimal control of random fields, this book exposes some important aspects of those fields for systems modeled by stochastic partial differential equations. It contains many results of interest to specialists in both the theory of random fields and optimal control theory who use modern mathematical tools for resolving specific applied problems, and presents research that has not previously been covered. More generally, this book is intended for scientists, graduate, and post-graduates specializing in probability theory and mathematical statistics.The models presented describe many processes in turbulence theory, fluid mechanics, hydrology, astronomy, and meteorology, and are widely used in pattern recognition theory and parameter identification of stochastic systems. Therefore, this book may also be useful to applied mathematicians who use probability and statistical methods in the selection of useful signals subject to noise, hypothesis distinguishing, distributed parameter systems optimal control, and more. Material presented in this monograph can be used for education courses on the estimation and control theory of random fields.

Stochastic Modeling and Optimization Methods for Critical Infrastructure Protection is a thorough exploration of mathematical models and tools that are designed to strengthen critical infrastructures against threats – both natural and adversarial. Divided into two volumes, this first volume examines stochastic modeling across key economic sectors and their interconnections, while the second volume focuses on advanced mathematical methods for enhancing infrastructure protection.The book covers a range of themes, including risk assessment techniques that account for systemic interdependencies within modern technospheres, the dynamics of uncertainty, instability and system vulnerabilities. The book also presents other topics such as cryptographic information protection and Shannon's theory of secret systems, alongside solutions arising from optimization, game theory and machine learning approaches.Featuring research from international collaborations, this book covers both theory and applications, offering vital insights for advanced risk management curricula. It is intended not only for researchers, but also educators and professionals in infrastructure protection and stochastic optimization.