Bayesian Reliability (Springer Series in Statistics)

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: <ul>
<li>Goodness-of-fit and model selection methods</li> <li>Hierarchical models for reliability estimation</li> <li>Fault tree analysis methodology that supports data acquisition at all levels in the tree</li> <li>Bayesian networks in reliability analysis</li> <li>Analysis of failure count and failure time data collected from repairable systems, and the assessment of various related performance criteria</li> <li>Analysis of nondestructive and destructive degradation data</li> <li>Optimal design of reliability experiments</li> <li>Hierarchical reliability assurance testing</li>
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Science & Math

Hamada, Michael S.

Wilson, Alyson

Reese, C. Shane

Martz, Harry

Mathematics

Springer

This book is 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. It can also be used as a textbook and contains more than 160 exercises.

In ancient times Clapham was a secluded Surrey village, clustered round a manor house and church. By the seventeenth century, the wealthy of London were building houses here as rural retreats and, by 1800, their mansions in extensive grounds surrounded Clapham Common, which was becoming a place of recreation. The development of the railways in the nineteenth century meant that families who could afford large houses moved further out to the countryside, selling off their ground to developers who built the terraced houses which cover the area today. Around forty years ago, after a period of neglect and bomb damage from the Second World War, the area started to become ‘gentrified’ and is now a desirable London suburb.Clapham Through Time traces these changes over the years. A few mansions have survived and been put to new uses, several churches lost during the war have been replaced, Clapham Common is recognisable though used quite differently, but the greatest change is in the appearance of the ordinary streets – the shop fronts, vehicles and people. Many who have arrived in Clapham during the last few years will be amazed by its former appearance!

Clapham Through Time

Fry, Claire

History

Amberley Publishing

A clear methodological and philosophical introduction to complexity theory as applied to urban and regional systems is given, together with a detailed series of modelling case studies compiled over the last couple of decades. Based on the new complex systems thinking, mathematical models are developed which attempt to simulate the evolution of towns, cities, and regions and the complicated co-evolutionary interaction there is both between and within them. The aim of these models is to help policy analysis and decision-making in urban and regional planning, energy policy, transport policy, and many other areas of service provision, infrastructure planning, and investment that are necessary for a successful society.

Politics & Social Sciences

Sociology

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