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A Guide to Simulation
Simulation means driving a model of a system with suitable inputs and observing the corresponding outputs. It is widely applied in engineering, in business, and in the physical and social sciences. Simulation method ology araws on computer. science, statistics, and operations research and is now sufficiently developed and coherent to be called a discipline in its own right. A course in simulation is an essential part of any operations re search or computer science program. A large fraction of applied work in these fields involves simulation; the techniques of simulation, as tools, are as fundamental as those of linear programming or compiler construction, for example. Simulation sometimes ap...
Principles and Practices of Interconnection Networks
One of the greatest challenges faced by designers of digital systems is optimizing the communication and interconnection between system components. Interconnection networks offer an attractive and economical solution to this communication crisis and are fast becoming pervasive in digital systems. Current trends suggest that this communication bottleneck will be even more problematic when designing future generations of machines. Consequently, the anatomy of an interconnection network router and science of interconnection network design will only grow in importance in the coming years.This book offers a detailed and comprehensive presentation of the basic principles of interconnection network...
An Annotated Timeline of Operations Research
An Annotated Timeline of Operations Research: An Informal History recounts the evolution of Operations Research (OR) as a new science - the science of decision making. Arising from the urgent operational issues of World War II, the philosophy and methodology of OR has permeated the resolution of decision problems in business, industry, and government. The Timeline chronicles the history of OR in the form of self-contained, expository entries. Each entry presents a concise explanation of the events and people under discussion, and provides key sources where further relevant information can be obtained. In addition, books and papers that have influenced the development of OR or helped to educa...
Random Number Generation and Monte Carlo Methods
Monte Carlo simulation has become one of the most important tools in all fields of science. This book surveys the basic techniques and principles of the subject, as well as general techniques useful in more complicated models and in novel settings. The emphasis throughout is on practical methods that work well in current computing environments.
Collective Agency and Cooperation in Natural and Artificial Systems
This book brings together philosophical approaches to cooperation and collective agency with research into human-machine interaction and cooperation from engineering, robotics, computer science and AI. Bringing these so far largely unrelated fields of study together leads to a better understanding of collective agency in natural and artificial systems and will help to improve the design and performance of hybrid systems involving human and artificial agents. Modeling collective agency with the help of computer simulations promises also philosophical insights into the emergence of collective agency. The volume consists of four sections. The first section is dedicated to the concept of agency. The second section of the book turns to human-machine cooperation. The focus of the third section is the transition from cooperation to collective agency. The last section concerns the explanatory value of social simulations of collective agency in the broader framework of cultural evolution.
Numerical Methods of Statistics
This book explains how computer software is designed to perform the tasks required for sophisticated statistical analysis. For statisticians, it examines the nitty-gritty computational problems behind statistical methods. For mathematicians and computer scientists, it looks at the application of mathematical tools to statistical problems. The first half of the book offers a basic background in numerical analysis that emphasizes issues important to statisticians. The next several chapters cover a broad array of statistical tools, such as maximum likelihood and nonlinear regression. The author also treats the application of numerical tools; numerical integration and random number generation are explained in a unified manner reflecting complementary views of Monte Carlo methods. Each chapter contains exercises that range from simple questions to research problems. Most of the examples are accompanied by demonstration and source code available from the author's website. New in this second edition are demonstrations coded in R, as well as new sections on linear programming and the Nelder–Mead search algorithm.
A Guide to Experimental Algorithmics
This is a guidebook for those who want to use computational experiments to support their work in algorithm design and analysis. Numerous case studies and examples show how to apply these concepts. All the necessary concepts in computer architecture and data analysis are covered so that the book can be used by anyone who has taken a course or two in data structures and algorithms.
Intelligent Autonomous Systems
This book contains scientific and engineering activities of the fifth international conference of Intelligent Autonomous Systems (IAS-5). The exploration for automatic systems has much attention over the centuries and created attractive research activities. The Intelligent and Autonomous systems are the current trend toward fully automatic systems that can adapt to changes in their environment. The purpose of the fifth IAS conference is to provide an opportunity for the international community of researchers in the field of autonomous systems as well as architectures, tools, components, techniques, and new IAS design methodologies. The emphasis will be on science and technology for autonomous systems working in a complex environment.
Linear Programming for Beginners
This book fills a gap in the linear programming literature, by explaining the steps that are illustrated but not always fully explained in every elementary operations book - the steps that lead from the elementary and intuitive graphical method of solution to the more advanced simplex tableau method. Most of the world, even those technically trained, can get along very well by seeing a few illustrations of simple linear programming problems solved graphically, followed by instruction in the use of computer software for solving real-world problems. But there needs to be a coterie of initiates who understand the process well enough to explain it to others, to know what the pitfalls, ramifications and special cases are, and to provide further developments. I have used an informal narrative style with a number of worked out examples and detailed explanations, to put the topic within reach.
Environmental Statistics and Data Analysis
This easy-to-understand introduction emphasizes the areas of probability theory and statistics that are important in environmental monitoring, data analysis, research, environmental field surveys, and environmental decision making. It communicates basic statistical theory with very little abstract mathematical notation, but without omitting importa
