As fatigue and fracture mechanics approaches are used more often for determining the useful life and/or inspection intervals for complex structures, realization sets-in that all factors are not well known or characterized. Indeed, inherent scatter exists in initial material quality and in material performance. Furthermore, projections of component usage in determination of applied stresses are inexact at best and are subject to much discrepancy between projected and actual usage. Even the models for predicting life contain inherent sources of error based on assumptions and/or empirically fitted parameters. All of these factors need to be accounted for to determine a distribution of potential lives based on combination of the aforementioned variables, as well as other factors. The purpose of this symposium was to create a forum for assessment of the state-of-the-art in incorporating these uncertainties and inherent scatter into systematic probabilistic methods for conducting life assessment.

Life can be unpredictable. And the more you can predict, the more control you will have over your own life. From calculating the health risks of smoking a pack of cigarettes a day to deciding on the best investments for your money, probabilities play a part in nearly all aspects of everyday life. Now, physics professor John D. McGervey puts all the facts and figures at your fingertips to help you make savvy, informed choices at home, at work, and at play. You will learn how the author believes you can: * Increase your chances of winning blackjack, contract bridge, horse racing, sports betting, and more * Get the most for your dollar when investing or buying insurance * Judge the risks of such common activities as smoking, using drugs, owning a handgun, and driving without a seat belt * Avoid faulty gambling systems and identify misleading statistics that can be used to draw you into poor investments * And much more. Inside you'll find a lively, entertaining, enlightening approach to minimizing your risks and maximizing your results -- simple strategies designed to give you the edge in life.

A revised edition that explores random numbers, probability, and statistical inference at an introductory mathematical level Written in an engaging and entertaining manner, the revised and updated second edition of Probably Not continues to offer an informative guide to probability and prediction. The expanded second edition contains problem and solution sets. In addition, the book’s illustrative examples reveal how we are living in a statistical world, what we can expect, what we really know based upon the information at hand and explains when we only think we know something. The author introduces the principles of probability and explains probability distribution functions. The book covers combined and conditional probabilities and contains a new section on Bayes Theorem and Bayesian Statistics, which features some simple examples including the Presecutor’s Paradox, and Bayesian vs. Frequentist thinking about statistics. New to this edition is a chapter on Benford’s Law that explores measuring the compliance and financial fraud detection using Benford’s Law. This book: Contains relevant mathematics and examples that demonstrate how to use the concepts presented Features a new chapter on Benford’s Law that explains why we find Benford’s law upheld in so many, but not all, natural situations Presents updated Life insurance tables Contains updates on the Gantt Chart example that further develops the discussion of random events Offers a companion site featuring solutions to the problem sets within the book Written for mathematics and statistics students and professionals, the updated edition of Probably Not: Future Prediction Using Probability and Statistical Inference, Second Edition combines the mathematics of probability with real-world examples. LAWRENCE N. DWORSKY, PhD, is a retired Vice President of the Technical Staff and Director of Motorola’s Components Research Laboratory in Schaumburg, Illinois, USA. He is the author of Introduction to Numerical Electrostatics Using MATLAB from Wiley.

Many engineers effect "probabilistic life prediction" by replacing constants with probability distributions and carefully modeling the physical relationships among the parameters. Surprisingly, the statistical relationships among the "constants" are often given short shrift, if not ignored altogether. Few recognize that while this simple substitution of distributions for constants will indeed produce a nondeterministic result, the corresponding "probabilities" are often woefully inaccurate. In fact, even the "trend" can be wrong, so these results can't even be used for sensitivity studies. This paper explores the familiar Paris equation relating crack growth rate and applied stress intensity to illustrate many statistical realities that are often ignored by otherwise careful engineers. Although the examples are Monte Carlo, the lessons also apply to other methods of probabilistic life prediction, including FORM/SORM (First/Second Order Reliability Method) and related "fast probability integration" methods.

How can we predict the future without asking an astrologer? When a phenomenon is not evolving, experiments can be repeated and observations therefore accumulated; this is what we have done in Volume I. However history does not repeat itself. Prediction of the future can only be based on the evolution observed in the past. Yet certain phenomena are stable enough so that observation in a sufficient interval of time gives usable information on the future or the mechanism of evolution. Technically, the keys to asymptotic statistics are the following: laws of large numbers, central limit theorems, and likelihood calculations. We have sought the shortest route to these theorems by neglecting to present the most general models. The future statistician will use the foundations of the statistics of processes and should satisfy himself about the unity of the methods employed. At the same time, we have adhered as closely as possible to present day ideas of the theory of processes. For those who wish to follow the study of probabilities to postgraduate level, it is not a waste of time to begin with the least difficult technical situations. This book for final year mathematics courses is not the end of the matter. It acts as a springboard either for dealing concretely with the problems of the statistics of processes, or viii In trod uction to study in depth the more subtle aspects of probabilities.

This set contains: 9780470040010 Probabilities: The Little Numbers That Rule Our Lives by Peter Olofsson and 9780470184011 Probably Not: Future Prediction Using Probability and Statistical Inference by Lawrence N. Dworsky