Bayesian Inference for Probabilistic Risk Assessment A Practitioner'
Bayesian Inference for Probabilistic Risk Assessment provides a Bayesian foundation for framing probabilistic problems and performing inference on these problems. Inference in the book employs a modern computational approach known as Markov chain Monte Ca
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Dana Kelly Curtis Smith •
Bayesian Inference for Probabilistic Risk Assessment A Practitioner’s Guidebook
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Dana Kelly Idaho National Laboratory (INL) PO Box 1625 Idaho Falls, ID 83415-3850 USA e-mail: [email protected]
Curtis Smith Idaho National Laboratory (INL) PO Box 1625 Idaho Falls, ID 83415-3850 USA e-mail: [email protected]
ISSN 1614-7839 ISBN 978-1-84996-186-8 DOI 10.1007/978-1-84996-187-5
e-ISBN 978-1-84996-187-5
Springer London Dordrecht Heidelberg New York British Library Cataloguing in Publication Data A Catalogue record for this book is available from the British Library Ó Springer-Verlag London Limited 2011 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms of licenses issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. The use of registered names, trademarks, etc., in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant laws and regulations and therefore free for general use. The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made. Cover design: eStudio Calamar, Berlin/Figueres Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Preface
This book began about 23 years ago, when one of the authors encountered a formula in a PRA procedure for estimating a probability of failure on demand, p. The formula was not the obvious ratio of number of failures to number of demands; instead it looked like this: ~ p¼
x þ 0:5 nþ1
Upon consulting more senior colleagues, he was told that this formula was the result of ‘‘performing a Bayesian update of a noninformative prior.’’ Due to the author’s ignorance of Bayesian inference, this statement was itself quite noninformative. And so began what has become a career-long interest in all things Bayesian. Both authors have indulged in much self-study over the years, along with a few graduate courses in Bayesian statistics, where they could be found. Along the way, we have developed training courses in Bayesian parameter estimation for the U.S. Nuclear Regulatory Commission and the National Aeronautics and Space Administration, and we continue to teach the descendants of these courses today. We have also developed and presented workshops in Bayesian inference for aging models, and written a number of journal articles and conference papers on the subject of Bayesian inference, all from the perspective of prac
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