(接132楼) 69.Reliability and Risk: A Bayesian Perspective
By Nozer D. Singpurwalla
Hardcover: 396 pages
Publisher: Wiley; 1 edition (October 6, 2006)
Language: English
ISBN-10: 0470855029
ISBN-13: 978-0470855027
Product Dimensions: 9.8 x 6.8 x 1.1 inches Book Description:
We all like to know how reliable and how risky certain situations are, and our increasing reliance on technology has led to the need for more precise assessments than ever before. Such precision has resulted in efforts both to sharpen the notions of risk and reliability, and to quantify them. Quantification is required for normative decision-making, especially decisions pertaining to our safety and wellbeing. Increasingly in recent years Bayesian methods have become key to such quantifications.
Reliability and Risk provides a comprehensive overview of the mathematical and statistical aspects of risk and reliability analysis, from a Bayesian perspective. This book sets out to change the way in which we think about reliability and survival analysis by casting them in the broader context of decision-making. This is achieved by:
Providing a broad coverage of the diverse aspects of reliability, including: multivariate failure models, dynamic reliability, event history analysis, non-parametric Bayes, competing risks, co-operative and competing systems, and signature analysis.
Covering the essentials of Bayesian statistics and exchangeability, enabling readers who are unfamiliar with Bayesian inference to benefit from the book.
Introducing the notion of “composite reliability”, or the collective reliability of a population of items.
Discussing the relationship between notions of reliability and survival analysis and econometrics and financial risk.
Reliability and Risk can most profitably be used by practitioners and research workers in reliability and survivability as a source of information, reference, and open problems. It can also form the basis of a graduate level course in reliability and risk analysis for students in statistics, biostatistics, engineering (industrial, nuclear, systems), operations research, and other mathematically oriented scientists, wherein the instructor could supplement the material with examples and problems.
(接132楼) 74.Bayesian Statistical Modelling
Bayesian Statistical Modelling (Wiley Series in Probability and Statistics) (Hardcover)
By Peter Congdon
Hardcover: 596 pages
Publisher: Wiley; 2 edition (January 17, 2007)
Language: English
ISBN-10: 0470018755
ISBN-13: 978-0470018750
Book Dimensions: 9.8 x 6.8 x 1.6 inches Book Description:
Bayesian methods combine the evidence from the data at hand with previous quantitative knowledge to analyse practical problems in a wide range of areas. The calculations were previously complex, but it is now possible to routinely apply Bayesian methods due to advances in computing technology and the use of new sampling methods for estimating parameters. Such developments together with the availability of freeware such as WINBUGS and R have facilitated a rapid growth in the use of Bayesian methods, allowing their application in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics.
Following the success of the first edition, this reworked and updated book provides an accessible approach to Bayesian computing and analysis, with an emphasis on the principles of prior selection, identification and the interpretation of real data sets.
The second edition:
Provides an integrated presentation of theory, examples, applications and computer algorithms.
Discusses the role of Markov Chain Monte Carlo methods in computing and estimation.
Includes a wide range of interdisciplinary applications, and a large selection of worked examples from the health and social sciences.
Features a comprehensive range of methodologies and modelling techniques, and examines model fitting in practice using Bayesian principles.
Provides exercises designed to help reinforce the reader’s knowledge and a supplementary website containing data sets and relevant programs.
Bayesian Statistical Modelling is ideal for researchers in applied statistics, medical science, public health and the social sciences, who will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a great source of reference for both researchers and students.
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