Bayesian Inference And Computation In Reliability And Survival Analysis

Author : Yuhlong Lio,Ding-Geng Chen,Hon Keung Tony Ng,Tzong-Ru Tsai
Publisher : Springer Nature
Page : 367 pages
File Size : 52,7 Mb
Release : 2022-08-01
Category : Mathematics
ISBN : 9783030886585

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Bayesian Inference and Computation in Reliability and Survival Analysis by Yuhlong Lio,Ding-Geng Chen,Hon Keung Tony Ng,Tzong-Ru Tsai Pdf

Bayesian analysis is one of the important tools for statistical modelling and inference. Bayesian frameworks and methods have been successfully applied to solve practical problems in reliability and survival analysis, which have a wide range of real world applications in medical and biological sciences, social and economic sciences, and engineering. In the past few decades, significant developments of Bayesian inference have been made by many researchers, and advancements in computational technology and computer performance has laid the groundwork for new opportunities in Bayesian computation for practitioners. Because these theoretical and technological developments introduce new questions and challenges, and increase the complexity of the Bayesian framework, this book brings together experts engaged in groundbreaking research on Bayesian inference and computation to discuss important issues, with emphasis on applications to reliability and survival analysis. Topics covered are timely and have the potential to influence the interacting worlds of biostatistics, engineering, medical sciences, statistics, and more. The included chapters present current methods, theories, and applications in the diverse area of biostatistical analysis. The volume as a whole serves as reference in driving quality global health research.

Author : Nozer D. Singpurwalla
Publisher : John Wiley & Sons
Page : 396 pages
File Size : 52,6 Mb
Release : 2006-08-14
Category : Mathematics
ISBN : 9780470060339

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Reliability and Risk by Nozer D. Singpurwalla Pdf

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.

Author : Joseph G. Ibrahim,Ming-Hui Chen,Debajyoti Sinha
Publisher : Springer Science & Business Media
Page : 494 pages
File Size : 48,9 Mb
Release : 2013-03-09
Category : Medical
ISBN : 9781475734478

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Bayesian Survival Analysis by Joseph G. Ibrahim,Ming-Hui Chen,Debajyoti Sinha Pdf

Survival analysis arises in many fields of study including medicine, biology, engineering, public health, epidemiology, and economics. This book provides a comprehensive treatment of Bayesian survival analysis. It presents a balance between theory and applications, and for each class of models discussed, detailed examples and analyses from case studies are presented whenever possible. The applications are all from the health sciences, including cancer, AIDS, and the environment.

Author : Joseph G. Ibrahim,Ming-Hui Chen,Debajyoti Sinha
Publisher : Unknown
Page : 496 pages
File Size : 50,7 Mb
Release : 2014-01-15
Category : Electronic
ISBN : 1475734484

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Bayesian Survival Analysis by Joseph G. Ibrahim,Ming-Hui Chen,Debajyoti Sinha Pdf

Author : Michael S. Hamada,Alyson Wilson,C. Shane Reese,Harry Martz
Publisher : Springer Science & Business Media
Page : 445 pages
File Size : 47,5 Mb
Release : 2008-08-15
Category : Mathematics
ISBN : 9780387779508

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Bayesian Reliability by Michael S. Hamada,Alyson Wilson,C. Shane Reese,Harry Martz Pdf

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

Author : Md. Rezaul Karim,M. Ataharul Islam
Publisher : Springer
Page : 252 pages
File Size : 41,9 Mb
Release : 2019-08-09
Category : Medical
ISBN : 9789811397769

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Reliability and Survival Analysis by Md. Rezaul Karim,M. Ataharul Islam Pdf

This book presents and standardizes statistical models and methods that can be directly applied to both reliability and survival analysis. These two types of analysis are widely used in many fields, including engineering, management, medicine, actuarial science, the environmental sciences, and the life sciences. Though there are a number of books on reliability analysis and a handful on survival analysis, there are virtually no books on both topics and their overlapping concepts. Offering an essential textbook, this book will benefit students, researchers, and practitioners in reliability and survival analysis, reliability engineering, biostatistics, and the biomedical sciences.

Author : Harry F. Martz,Ray A. Waller
Publisher : Unknown
Page : 778 pages
File Size : 48,8 Mb
Release : 1982-05-14
Category : Mathematics
ISBN : MINN:31951000486757M

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Bayesian Reliability Analysis by Harry F. Martz,Ray A. Waller Pdf

A comprehensive collection of and introduction to the major advances in Bayesian reliability analysis techniques developed during the last two decades, in textbook form. Focuses primary attention on the exponential, Weibull, normal, log normal, inverse Gaussian, and gamma failure time distributions, as well as the binomial, Pascal, and Poisson sampling models. Noninformative and natural conhugate prior distributions are emphasized, although other classes or prior distributions are also often considered. Background chapters on probability, statistics, and classical reliability analysis methods are also included.

Author : Anonim
Publisher : Elsevier
Page : 1062 pages
File Size : 42,8 Mb
Release : 2005-11-29
Category : Mathematics
ISBN : 9780080461175

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Bayesian Thinking, Modeling and Computation by Anonim Pdf

This volume describes how to develop Bayesian thinking, modelling and computation both from philosophical, methodological and application point of view. It further describes parametric and nonparametric Bayesian methods for modelling and how to use modern computational methods to summarize inferences using simulation. The book covers wide range of topics including objective and subjective Bayesian inferences with a variety of applications in modelling categorical, survival, spatial, spatiotemporal, Epidemiological, software reliability, small area and micro array data. The book concludes with a chapter on how to teach Bayesian thoughts to nonstatisticians. Critical thinking on causal effects Objective Bayesian philosophy Nonparametric Bayesian methodology Simulation based computing techniques Bioinformatics and Biostatistics

Author : N. Limnios,M. Nikulin
Publisher : Springer Science & Business Media
Page : 515 pages
File Size : 51,8 Mb
Release : 2012-12-06
Category : Technology & Engineering
ISBN : 9781461213840

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Recent Advances in Reliability Theory by N. Limnios,M. Nikulin Pdf

This book presents thirty-one extensive and carefully edited chapters providing an up-to-date survey of new models and methods for reliability analysis and applications in science, engineering, and technology. The chapters contain broad coverage of the latest developments and innovative techniques in a wide range of theoretical and numerical issues in the field of statistical and probabilistic methods in reliability.

Author : Alyson G. Wilson
Publisher : World Scientific
Page : 430 pages
File Size : 50,8 Mb
Release : 2005
Category : Mathematics
ISBN : 9789812563569

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Modern Statistical and Mathematical Methods in Reliability by Alyson G. Wilson Pdf

This volume contains extended versions of 28 carefully selected and reviewed papers presented at The Fourth International Conference on Mathematical Methods in Reliability in Santa Fe, New Mexico, June 21-25, 2004, the leading conference in reliability research. A broad overview of current research activities in reliability theory and its applications is provided with coverage on reliability modeling, network and system reliability, Bayesian methods, survival analysis, degradation and maintenance modeling, and software reliability. The contributors are all leading experts in the field and include the plenary session speakers, Tim Bedford, Thierry Duchesne, Henry Wynn, Vicki Bier, Edsel Pena, Michael Hamada, and Todd Graves.

Author : Satyanshu K. Upadhyay,Umesh Singh,Dipak Dey
Publisher : Anshan Pub
Page : 528 pages
File Size : 45,7 Mb
Release : 2007
Category : Mathematics
ISBN : STANFORD:36105123399649

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Bayesian Statistics and Its Applications by Satyanshu K. Upadhyay,Umesh Singh,Dipak Dey Pdf

In the last two decades, Bayesian Statistics has acquired immense importance and has penetrated almost every area including those where the application of statistics appeared to be a remote possibility. This volume provides both theoretical and practical insights into the subject with detailed up-to-date material on various aspects. It serves two important objectives - to offer a thorough background material for theoreticians and gives a variety of applications for applied statisticians and practitioners. Consisting of 33 chapters, it covers topics on biostatistics, econometrics, reliability, image analysis, Bayesian computation, neural networks, prior elicitation, objective Bayesian methodologies, role of randomisation in Bayesian analysis, spatial data analysis, nonparametrics and a lot more. The book will serve as an excellent reference work for updating knowledge and for developing new methodologies in a wide variety of areas. It will become an invaluable tool for statisticians and the practitioners of Bayesian paradigm.

Author : Richard E. Barlow,C.A. Claroti,Fabio Spizzichino
Publisher : CRC Press
Page : 396 pages
File Size : 49,9 Mb
Release : 1993-09-01
Category : Business & Economics
ISBN : 0412534800

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Reliability and Decision Making by Richard E. Barlow,C.A. Claroti,Fabio Spizzichino Pdf

First published in 1993. Routledge is an imprint of Taylor & Francis, an informa company.

Author : Jim Albert
Publisher : Springer Science & Business Media
Page : 300 pages
File Size : 53,7 Mb
Release : 2009-04-20
Category : Mathematics
ISBN : 9780387922980

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Bayesian Computation with R by Jim Albert Pdf

There has been dramatic growth in the development and application of Bayesian inference in statistics. Berger (2000) documents the increase in Bayesian activity by the number of published research articles, the number of books,andtheextensivenumberofapplicationsofBayesianarticlesinapplied disciplines such as science and engineering. One reason for the dramatic growth in Bayesian modeling is the availab- ity of computational algorithms to compute the range of integrals that are necessary in a Bayesian posterior analysis. Due to the speed of modern c- puters, it is now possible to use the Bayesian paradigm to ?t very complex models that cannot be ?t by alternative frequentist methods. To ?t Bayesian models, one needs a statistical computing environment. This environment should be such that one can: write short scripts to de?ne a Bayesian model use or write functions to summarize a posterior distribution use functions to simulate from the posterior distribution construct graphs to illustrate the posterior inference An environment that meets these requirements is the R system. R provides a wide range of functions for data manipulation, calculation, and graphical d- plays. Moreover, it includes a well-developed, simple programming language that users can extend by adding new functions. Many such extensions of the language in the form of packages are easily downloadable from the Comp- hensive R Archive Network (CRAN).

Author : Nozer D. Singpurwalla
Publisher : Wiley
Page : 544 pages
File Size : 45,9 Mb
Release : 2019-08-19
Category : Mathematics
ISBN : 0470746270

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Reliability, Risk and Survival by Nozer D. Singpurwalla Pdf

Risk assessment and risk analysis are now firmly fixed in the biostatistician’s and engineer's lexicon. Reliability is the other key element in the mix for smooth running projects and operations. In the modern industrial era, economic factors have resulted in the construction and operation of larger and more complex process plant. Engineers are working to maximize the benefits of modern processing technology while reducing the safety risks to acceptable levels. However, each processing plant has unique problems and each must be individually assessed to identify, evaluate and control associated hazards. Statistical methods play a key role in the quantification of reliability, and since the advent of MCMC, Bayesian methods have become increasingly important. This book addresses the need for a sound introduction to the mathematical and statistical aspects of reliability analysis from a Bayesian perspective. It features many real examples, taken from the author’s vast experience, and lots of applications from reliability engineering. The author is well respected in both the statistical/Bayesian and reliability communities.

Author : Riten Mitra,Peter Müller
Publisher : Springer
Page : 448 pages
File Size : 51,6 Mb
Release : 2015-07-25
Category : Medical
ISBN : 9783319195186

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Nonparametric Bayesian Inference in Biostatistics by Riten Mitra,Peter Müller Pdf

As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters cover: clinical trials, spatial inference, proteomics, genomics, clustering, survival analysis and ROC curve.