Certainty exists only in idealized models. Viewed as the quantification of uncertainties, probabilitry and random processes play a significant role in modern engineering, particularly in areas such as structural dynamics. Unlike this book, however, few texts develop applied probability in the practical manner appropriate for engineers.

Probability Models in Engineering and Science provides a comprehensive, self-contained introduction to applied probabilistic modeling. The first four chapters present basic concepts in probability and random variables, and while doing so, develop methods for static problems. The remaining chapters address dynamic problems, where time is a critical parameter in the randomness. Highlights of the presentation include numerous examples and illustrations and an engaging, human connection to the subject, achieved through short biographies of some of the key people in the field. End-of-chapter problems help solidify understanding and footnotes to the literature expand the discussions and introduce relevant journals and texts.

This book builds the background today's engineers need to deal explicitly with the scatter observed in experimental data and with intricate dynamic behavior. Designed for undergraduate and graduate coursework as well as self-study, the text's coverage of theory, approximation methods, and numerical methods make it equally valuable to practitioners.

Benaroya (mechanical engineering, Rutgers University) and Han (mechanical engineering, Texas Tech University) provide a self- contained introduction to applied probabilistic modeling. The first four chapters present basic concepts in probability and random variables, and develop methods for static problems. The remaining chapters address dynamic problems, where time is a critical parameter in the randomness. Highlights of the presentation include numerous examples and illustrations, short biographies of key figures in the field, and chapter problems. The book is appropriate for undergraduate and graduate courses.
Annotation ©2005 Book News, Inc., Portland, OR (booknews.com)

Benaroya (mechanical engineering, Rutgers University) and Han (mechanical engineering, Texas Tech University) provide a self- contained introduction to applied probabilistic modeling. The first four chapters present basic concepts in probability and random variables, and develop methods for static problems. The remaining chapters address dynamic problems, where time is a critical parameter in the randomness. Highlights of the presentation include numerous examples and illustrations, short biographies of key figures in the field, and chapter problems. The book is appropriate for undergraduate and graduate courses. Annotation ©2005 Book News, Inc., Portland, OR (booknews.com)

Certainty exists only in idealized models. Viewed as the quantification of uncertainties, probabilitry and random processes play a significant role in modern engineering, particularly in areas such as structural dynamics. Unlike this book, however, few texts develop applied probability in the practical manner appropriate for engineers. Probability Models in Engineering and Science provides a comprehensive, self-contained introduction to applied probabilistic modeling. The first four chapters present basic concepts in probability and random variables, and while doing so, develop methods for static problems. The remaining chapters address dynamic problems, where time is a critical parameter in the randomness. Highlights of the presentation include numerous examples and illustrations and an engaging, human connection to the subject, achieved through short biographies of some of the key people in the field.

Probabilistic Models in Engineering and Science provides engineers, scientists, and students with a self-contained, comprehensive introduction to applied probabilistic modeling. Perfectly suited to undergraduate and graduate coursework, professional reference, or self-study, this book develops applied probability along with the historical context of the field, providing short biographies and portraits of key "names" mentioned in the book. The authors have included extensive example problems, ample end-of-chapter problems, and footnotes that provide references for further in-depth information. Topics include random processes, reliability, and the Monte Carlo method.