Alyson Wilson – författare

All the data was out there to warn us of this impending attack, why didn''t we see it?" This was a frequently asked question in the weeks and months after the terrorist attacks on the World Trade Center and the Pentagon on September 11, 2001. In the wake of the attacks, statisticians moved quickly to become part of the national response to the global war on terror. This book is an overview of the emerging research program at the intersection of national security and statistical sciences. A wide range of talented researchers address issues in

- Syndromic Surveillance---How do we detect and recognize bioterrorist events?

- Modeling and Simulation---How do we better understand and explain complex processes so that decision makers can take the best course of action?

- Biometric Authentication---How do we pick the terrorist out of the crowd of faces or better match the passport to the traveler?

- Game Theory---How do we understand the rules that terrorists are playing by?

This book includes technical treatments of statistical issues that will be of use to quantitative researchers as well as more general examinations of quantitative approaches to counterterrorism that will be accessible to decision makers with stronger policy backgrounds.

Dr. Alyson G. Wilson is a statistician and the technical lead for DoD programs in the Statistical Sciences Group at Los Alamos National Laboratory. Dr. Gregory D. Wilson is a rhetorician and ethnographer in the Statistical Sciences Group at Los Alamos National Laboratory. Dr. David H. Olwell is chair of the Department of Systems Engineering at the Naval Postgraduate School in Monterey, California.

Bayesian Reliability presents modern methods and techniques for analyzing reliability data from a Bayesian perspective. The adoption and application of Bayesian methods in virtually all branches of science and engineering have significantly increased over the past few decades. This increase is largely due to advances in simulation-based computational tools for implementing Bayesian methods.

The authors extensively use such tools throughout this book, focusing on assessing the reliability of components and systems with particular attention to hierarchical models and models incorporating explanatory variables. Such models include failure time regression models, accelerated testing models, and degradation models. The authors pay special attention to Bayesian goodness-of-fit testing, model validation, reliability test design, and assurance test planning. Throughout the book, the authors use Markov chain Monte Carlo (MCMC) algorithms for implementing Bayesian analyses -- algorithms that make the Bayesian approach to reliability computationally feasible and conceptually straightforward.

This book is primarily a reference collection of modern Bayesian methods in reliability for use by reliability practitioners. There are more than 70 illustrative examples, most of which utilize real-world data. This book can also be used as a textbook for a course in reliability and contains more than 160 exercises.

Noteworthy highlights of the book include Bayesian approaches for the following: