This book focuses on the class of large-scale stochastic systems, which has dominated the attention of many academic and research groups. It discusses distributed-sensor networks, decentralized detection theory, and econometric models with integrated and decentralized policymakers.

This book is a revision of Stochastic Processes in Information and Dynamical Systems written by the first author (E.W.) and published in 1971. The book was originally written, and revised, to provide a graduate level text in stochastic processes for students whose primary interest is its applications. It treats both the traditional topic of sta tionary processes in linear time-invariant systems as well as the more modern theory of stochastic systems in which dynamic structure plays a profound role. Our aim is to provide a high-level, yet readily acces sible, treatment of those topics in the theory of continuous-parameter stochastic processes that are important in the analysis of information and dynamical systems. The theory of stochastic processes can easily become abstract. In dealing with it from an applied point of view, we have found it difficult to decide on the appropriate level of rigor. We intend to provide just enough mathematical machinery so that important results can be stated PREFACE vi with precision and clarity; so much ofthe theory of stochastic processes is inherently simple if the suitable framework is provided. The price of providing this framework seems worth paying even though the ul timate goal is in applications and not the mathematics per se.

Advancements in the technology and availability of data sources have led to the `Big Data' era. Working with large data offers the potential to uncover more fine-grained patterns and take timely and accurate decisions, but it also creates a lot of challenges such as slow training and scalability of machine learning models. One of the major challenges in machine learning is to develop efficient and scalable learning algorithms, i.e., optimization techniques to solve large scale learning problems. Stochastic Optimization for Large-scale Machine Learning identifies different areas of improvement and recent research directions to tackle the challenge. Developed optimisation techniques are also explored to improve machine learning algorithms based on data access and on first and second order optimisation methods. Key Features: Bridges machine learning and Optimisation. Bridges theory and practice in machine learning. Identifies key research areas and recent research directions to solve large-scale machine learning problems. Develops optimisation techniques to improve machine learning algorithms for big data problems. The book will be a valuable reference to practitioners and researchers as well as students in the field of machine learning.

"Bridges the gap between laboratory research and practical applications in industry and power utilities-clearly organized into three distinct sections that cover basic theories and concepts, execution of principles, and innovative new techniques. Includes new chapters detailing industrial uses and isues of hazard and safety, and review excercises to accompany each chpter."

"Covering virtually all areas of distribution engineering, this complete reference work examines the unique behavior of utilities and provides the practical knowledge necessary to solve real-world distribution problems. "

This newly revised and updated reference presents sensible approaches to the design, selection, and usage of high-voltage circuit breakers-highlighting compliance issues concerning new and aging equipment to the evolving standards set forth by the American National Standards Institute and the International Electrotechnical Commission. This edition