Students of statistics, operations research, and engineering will be informed of simulation methodology for problems in both mathematical statistics and systems simulation. This discussion presents many of the necessary statistical and graphical techniques. A discussion of statistical methods based on graphical techniques and exploratory data is among the highlights of Simulation Methodology for Statisticians, Operations Analysts, and Engineers. For students who only have a minimal background in statistics and probability theory, the first five chapters provide an introduction to simulation.
Students of statistics, operations research, and engineering will be informed of simulation methodology for problems in both mathematical statistics and systems simulation. This discussion presents many of the necessary statistical and graphical techniques. A discussion of statistical methods based on graphical techniques and exploratory data is among the highlights of Simulation Methodology for Statisticians, Operations Analysts, and Engineers. For students who only have a minimal background in statistics and probability theory, the first five chapters provide an introduction to simulation.
Students of statistics, operations research, and engineering will be informed of simulation methodology for problems in both mathematical statistics and systems simulation. This discussion presents many of the necessary statistical and graphical techniques. A discussion of statistical methods based on graphical techniques and exploratory data is among the highlights of Simulation Methodology for Statisticians, Operations Analysts, and Engineers. For students who only have a minimal background in statistics and probability theory, the first five chapters provide an introduction to simulation.
This book is devoted to a new branch of experimental design theory called simulation experimental design. There are many books devoted either to the theory of experimental design or to system simulation techniques, but in this book an approach to combine both fields is developed. Especially the mathematical theory of such universal variance reduction techniques as splitting and Russian Roulette is explored. The book contains a number of results on regression design theory related to nonlinear problems, the E-optimum criterion and designs which minimize bias. Audience: This volume will be of value to readers interested in systems simulation, applied statistics and numerical methods with basic knowledge of applied statistics and linear algebra.
The application areas of uncertainty are numerous and diverse, including all fields of engineering, computer science, systems control and finance. Determining appropriate ways and methods of dealing with uncertainty has been a constant challenge. The theme for this book is better understanding and the application of uncertainty theories. This book, with invited chapters, deals with the uncertainty phenomena in diverse fields. The book is an outgrowth of the Fourth International Symposium on Uncertainty Modeling and Analysis (ISUMA), which was held at the center of Adult Education, College Park, Maryland, in September 2003. All of the chapters have been carefully edited, following a review process in which the editorial committee scrutinized each chapter. The contents of the book are reported in twenty-three chapters, covering more than . . ... pages. This book is divided into six main sections. Part I (Chapters 1-4) presents the philosophical and theoretical foundation of uncertainty, new computational directions in neural networks, and some theoretical foundation of fuzzy systems. Part I1 (Chapters 5-8) reports on biomedical and chemical engineering applications. The sections looks at noise reduction techniques using hidden Markov models, evaluation of biomedical signals using neural networks, and changes in medical image detection using Markov Random Field and Mean Field theory. One of the chapters reports on optimization in chemical engineering processes.
Simulation is a multi-disciplinary field, and significant simulation research is dispersed across multiple fields of study. Distributed computer systems, software design methods, and new simulation techniques offer synergistic multipliers when joined together in a distributed simulation. Systems of most interest to the simulation practitioner are often the most difficult to model and implement. Distributed Simulation brings together the many complex technologies for distributed simulation. There is strong emphasis on emerging simulation methodologies, including object-oriented, multilevel, and multi-resolution simulation. Finally, one concise text provides a strong foundation for the development of high fidelity simulations in heterogeneous distributed computing environments!
Continuing the best-selling tradition of the Handbook of Structural Engineering, this second edition is a comprehensive reference to the broad spectrum of structural engineering, encapsulating the theoretical, practical, and computational aspects of the field. The contributors cover traditional and innovative approaches to analysis, design, and rehabilitation. New topics include: fundamental theories of structural dynamics; advanced analysis; wind- and earthquake-resistant design; design of prestressed structures; high-performance steel, concrete, and fiber-reinforced polymers; semirigid frame structures; structural bracing; and structural design for fire safety.
For many engineers, statistics is the method of last resort, when no deterministic method can be found to make sense of geological complexities. This volume shows that geological data and geology often have a mutually beneficial effect especially in the diagnosis of complex geological phenomena.
