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"Problems in probability comprises one of the most comprehensive, nearly encyclopedic, collections of problems and exercises in probability theory. Many problems contain helpful hints and other relevant comments and a portion of the material covers some important applications from optimal control and mathematical finance. Readers of diverse backgrounds-from students to researchers-will find a great deal of value in this book and can treat the work as an exercise manual, a handbook, or as a supplementary text to a course in probability theory, control, and mathematical finance. The problems and exercises in this book vary in nature and degree of difficulty. Some problems are meant to test the reader's basic understanding, others are of medium-to-high degrees of difficulty and require more creative thinking. Other problems are meant to develop additional theoretical concepts and tools or to familiarize the reader with various facts that are not necessarily covered in mainstream texts. Additional problems are related to the passage from random walk to Brownian motions and Brownian bridges. The appendix contains a summary of the main results, notation and terminology that are used throughout the book. It also contains additional material from combinatorics, potential theory and Markov chains-subjects that are not covered in the book, but are nevertheless needed for many of the exercises included here."

"Probability with statistical applications offers a practical introduction to probability at all levels with different backgrounds and views towards applications. It also introduces mathematical statistics."

"This book provides the reader with the basic skills and tools of statistics and probability in the context of engineering modeling and analysis. The emphasis is on the application and the reasoning behind the application of these skills and tools for the purpose of enhancing decision making in engineering.The purpose of the book is to ensure that the reader will acquire the required theoretical basis and technical skills such as to feel comfortable with the theory of basic statistics and probability. Moreover, in this book, as opposed to many standard books on the same subject, the perspective is to focus on the use of the theory for the purpose of engineering model building and decision making. This work is suitable for readers with little or no prior knowledge on the subject of statistics and probability."

"Recent advances in system reliability discusses developments in modern reliability theory such as signatures, multi-state systems and statistical inference. It describes the latest achievements in these fields, and covers the application of these achievements to reliability engineering practice. The chapters cover a wide range of new theoretical subjects and have been written by leading experts in reliability theory and its applications. The topics include, concepts and different definitions of signatures (D-spectra), their properties and applications to reliability of coherent systems and network-type structures, Lz-transform of Markov stochastic process and its application to multi-state system reliability analysis, methods for cost-reliability and cost-availability analysis of multi-state systems, optimal replacement and protection strategy, and statistical inference.Recent advances in system reliability presents many examples to illustrate the theoretical results. Real world multi-state systems, such as power generation and transmission, refrigeration, and production systems, are considered in the form of case studies."

"The solution of stochastic equations poses notable technical difficulties that are frequently circumvented by heuristic assumptions at the expense of accuracy and rigor. The main objective of Stochastic Systems is to promoting the development of accurate and efficient methods for solving stochastic equations and to foster interactions between engineers, scientists, and mathematicians. To achieve these objectives Stochastic Systems presents, a clear and brief review of essential concepts on probability theory, random functions, stochastic calculus, Monte Carlo simulation, and functional analysis, probabilistic models for random variables and functions needed to formulate stochastic equations describing realistic problems in engineering and applied sciences, practical methods for quantifying the uncertain parameters in the definition of stochastic equations, solving approximately these equations, and assessing the accuracy of approximate solutions."

"This book will expose the reader to some of the fundamental methodology in non-cooperative game theory, and highlight some numerical methods, along with some relevant applications. Since the early development days, differential game theory has had a significant impact in such diverse disciplines as applied mathematics, economics, systems theory, engineering, operations, research, biology, ecology, environmental sciences, among others. Modern game theory now relies on wide ranging mathematical and computational methods, and relevant applications that are rich and challenging. Game theory has been widely recognized as an important tool in many fields. Importance of game theory to economics is illustrated by the fact that numerous game theorists, such as John Forbes Nash, Jr., Robert J. Aumann and Thomas C. Schelling, have won the Nobel Memorial Prize in Economics Sciences. Simply put, game-theory has the potential to reshape the analysis of human interaction."

"This self-contained monograph presents a new stochastic approach to global optimization problems arising in a variety of disciplines including mathematics, operations research, engineering, and economics. The volume deals with constrained and unconstrained problems and puts a special emphasis on large scale problems. It also introduces a new unified concept for unconstrained, constrained, vector, and stochastic global optimization problems. All methods presented are illustrated by various examples. Practical numerical algorithms are given and analyzed in detail. The topics presented include the randomized curve of steepest descent, the randomized curve of dominated points, the semi-implicit Euler method, the penalty approach, and active set strategies. The optimal decoding of block codes in digital communications is worked out as a case study and shows the potential and high practical relevance of this new approach."

"This monograph and translation from the Russian describes in detail and comments on the fundamentals of metrology. The basic concepts of metrology, the principles of the International System of Units SI, the theory of measurement uncertainty, the new methodology of estimation of measurement accuracy on the basis of the uncertainty concept, as well as the methods for processing measurement results and estimating their uncertainty are discussed from the modern position. It is shown that the uncertainty concept is compatible with the classical theory of accuracy. The theory of random uncertainties is supplemented with their most general description on the basis of generalized normal distribution; the instrumental systematic errors are presented in connection with the methodology of normalization of the metrological characteristics of measuring instruments. The information about modern systems of traceability is given. All discussed theoretical principles and calculation methods are illustrated with examples."

"This self-contained, practical, entry-level text integrates the basic principles of applied mathematics, applied probability, and computational science for a clear presentation of stochastic processes and control for jump diffusions in continuous time. The author covers the important problem of controlling these systems and, through the use of a jump calculus construction, discusses the strong role of discontinuous and nonsmooth properties versus random properties in stochastic systems. The book emphasizes modeling and problem solving and presents sample applications in financial engineering and biomedical modeling. Computational and analytic exercises and examples are included throughout. While classical applied mathematics is used in most of the chapters to set up systematic derivations and essential proofs, the final chapter bridges the gap between the applied and the abstract worlds to give readers an understanding of the more abstract literature on jump diffusions."

"Modern financial mathematics relies on the theory of random processes in time, reflecting the erratic fluctuations in financial markets.This book introduces the fascinating area of financial mathematics and its calculus in an accessible manner geared toward undergraduate students. Using little high-level mathematics, the author presents the basic methods for evaluating financial options and building financial simulations."