Engineering Uncertainty and Risk Analysis: A Balanced Approach to Probability, Statistics, Stochastic Modeling, and Stochastic Differential Equations by Sergio E. Serrano

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Synopsis

Engineering Uncertainty and Risk Analysis offers an integrated coverage of the subjects of probability, statistics, Monte Carlo simulation, descriptive and inferential statistics, design of experiments, systems reliability, fitting random data to models, analysis of variance (ANOVA), stochastic processes, and stochastic differential equations. While these subjects are normally covered in different treatises or college courses, the author for the first time presents an introduction to the broad field of engineering uncertainty analysis in one comprehensive, friendly, coverage. The focus is on engineering applications, rather than theoretical or mathematical considerations. Each concept is illustrated with several examples (151 solved examples) of relevance in engineering applications (no cards, colored balls, or dice). Engineering Uncertainty and Risk Analysis illustrates practical applications with the use of modern computer algebra software, such as Maple (50 short computer programs). However, no prior knowledge of Maple is needed. The author gradually introduces the reader to the use of Maple in stochastic and statistical analysis and modeling, starting from the very basic operations through advanced operations.

Engineering Uncertainty and Risk Analysis presents random differential equations as a natural extension to their deterministic counterpart, rather than a special theoretical subject for statisticians or advanced graduate work. The author strives to emulate the courses of differential equations in most college textbooks. This way the reader may associate random differential equations naturally with the deterministic course s/he had. Many examples illustrate the concepts. In addition, new powerful analytical methods to solve non-linear equations are presented for the first time at this level.