Risk Analysis in Engineering and Economicsis required reading for decision making under conditions of uncertainty. The author describes the fundamental concepts, techniques, and applications of the subject in a style tailored to meet the needs of students and practitioners of engineering, science, economics, and finance. Drawing on his extensive experience in uncertainty and risk modeling and analysis, the author covers everything from basic theory and key computational algorithms to data needs, sources, and collection. He emphasizes practical use of the methods presented and carefully examines the limitations, advantages, and disadvantages of each to help readers translate the discussed techniques into real-world solutions.
This Second Edition:
Introduces the topic of risk finance Incorporates homeland security applications throughout Offers additional material on predictive risk management Includes a wealth of new and updated end-of-chapter problems Delivers a complementary mix of theoretical background and risk methods Brings together engineering and economics on balanced terms to enable appropriate decision making Presents performance segregation and aggregation within a risk framework Contains contemporary case studies, such as protecting hurricane-prone regions and critical infrastructure Provides 320+ tables and figures, over 110 diverse examples, numerous end-of-book references, and a bibliography
Unlike the classical books on reliability and risk management, Risk Analysis in Engineering and Economics, Second Edition relates underlying concepts to everyday applications, ensuring solid understanding and use of the methods of risk analysis.
Probability, Statistics, and Reliability for Engineers and Scientists, Third Edition introduces the fundamentals of probability, statistics, reliability, and risk methods to engineers and scientists for the purposes of data and uncertainty analysis and modeling in support of decision making.
The third edition of this bestselling text presents probability, statistics, reliability, and risk methods with an ideal balance of theory and applications. Clearly written and firmly focused on the practical use of these methods, it places increased emphasis on simulation, particularly as a modeling tool, applying it progressively with projects that continue in each chapter. This provides a measure of continuity and shows the broad use of simulation as a computational tool to inform decision making processes. This edition also features expanded discussions of the analysis of variance, including single- and two-factor analyses, and a thorough treatment of Monte Carlo simulation. The authors not only clearly establish the limitations, advantages, and disadvantages of each method, but also show that data analysis is a continuum rather than the isolated application of different methods.
Like its predecessors, this book continues to serve its purpose well as both a textbook and a reference. Ultimately, readers will find the content of great value in problem solving and decision making, particularly in practical applications.