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Author : Mitchell O. Locks
Publisher :
ISBN 13 :
Total Pages : 108 pages
Book Rating : 4.E/5 ( download)
Download or read book Monte Carlo Bayesian System Reliability written by Mitchell O. Locks and published by . This book was released on 1975 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author :
Publisher :
ISBN 13 :
Total Pages : 65 pages
Book Rating : 4.:/5 (227 download)
Download or read book Monte Carlo Bayesian System Reliability- and MTBF-Confidence Assessment, II. Volume 1. Theory written by and published by . This book was released on 1978 with total page 65 pages. Available in PDF, EPUB and Kindle. Book excerpt: SPARCS-2 (Simulation Program for Assessing the Reliabilities of Complex Systems, Version 2) is a PL/1 computer program for assessing (establishing interval estimates for) the reliability and the MTBF of a large and complex system of any modular configuration. The system can consist of a complex logical assembly of independently failing attribute (binomial-Bernoulli) and time-to-failure (Poisson-exponential) components, without any regard to their placement. Alternatively, it can be a configuration of independently failing modules, where each module has either or both attribute and time-to- failure components. The raw data for assessments are the component failure history data and the system configuration.
Author : Enrico Zio
Publisher : World Scientific Publishing Company
ISBN 13 : 9813107421
Total Pages : 363 pages
Book Rating : 4.8/5 (131 download)
Download or read book Computational Methods For Reliability And Risk Analysis written by Enrico Zio and published by World Scientific Publishing Company. This book was released on 2009-01-22 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book illustrates a number of modelling and computational techniques for addressing relevant issues in reliability and risk analysis. In particular, it provides: i) a basic illustration of some methods used in reliability and risk analysis for modelling the stochastic failure and repair behaviour of systems, e.g. the Markov and Monte Carlo simulation methods; ii) an introduction to Genetic Algorithms, tailored to their application for RAMS (Reliability, Availability, Maintainability and Safety) optimization; iii) an introduction to key issues of system reliability and risk analysis, like dependent failures and importance measures; and iv) a presentation of the issue of uncertainty and of the techniques of sensitivity and uncertainty analysis used in support of reliability and risk analysis.The book provides a technical basis for senior undergraduate or graduate courses and a reference for researchers and practitioners in the field of reliability and risk analysis. Several practical examples are included to demonstrate the application of the concepts and techniques in practice.
Author : Mitchell O. Locks
Publisher :
ISBN 13 :
Total Pages : 99 pages
Book Rating : 4.:/5 (227 download)
Download or read book Monte Carlo Bayesian System Reliability- and MTBF-Confidence Assessment written by Mitchell O. Locks and published by . This book was released on 1975 with total page 99 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report documents the underlying theory and the usage of computer software for providing both point and interval estimates of the reliability and the MTBF (Mean Time Between Failures) of a large and complex system of any Logical configuration whatsoever. Interval estimation is frequently called assessment. Every component is of one of two types, time-to-failure, subject to a Poisson process, or attribute type, subject to a Bernoulli process. The prior data consists of the system logical configuration information, such as the set of minimal paths of the set of minimal cuts for each module, and for the system as a configuration of independent modules, the failure-history data for every component, and the system mission time. For time-to-failure components, the failure-history data consists of total failures and total accumulated testing time; for attribute components (e.g., failure modes) the data are total accumulated tests and total failures.
Author : Mitchell O. Locks
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (85 download)
Download or read book Monte Carlo Bayesian System Reliability - and MTBF-confidence Assessment, II written by Mitchell O. Locks and published by . This book was released on 1978 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Michael S. Hamada
Publisher : Springer Science & Business Media
ISBN 13 : 0387779507
Total Pages : 445 pages
Book Rating : 4.3/5 (877 download)
Download or read book Bayesian Reliability written by Michael S. Hamada and published by Springer Science & Business Media. This book was released on 2008-08-15 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 :
Publisher :
ISBN 13 :
Total Pages : 252 pages
Book Rating : 4.:/5 (742 download)
Download or read book Mar de histórias written by and published by . This book was released on 1979 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Enrico Zio
Publisher : Springer Science & Business Media
ISBN 13 : 1447145887
Total Pages : 204 pages
Book Rating : 4.4/5 (471 download)
Download or read book The Monte Carlo Simulation Method for System Reliability and Risk Analysis written by Enrico Zio and published by Springer Science & Business Media. This book was released on 2012-11-02 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: Monte Carlo simulation is one of the best tools for performing realistic analysis of complex systems as it allows most of the limiting assumptions on system behavior to be relaxed. The Monte Carlo Simulation Method for System Reliability and Risk Analysis comprehensively illustrates the Monte Carlo simulation method and its application to reliability and system engineering. Readers are given a sound understanding of the fundamentals of Monte Carlo sampling and simulation and its application for realistic system modeling. Whilst many of the topics rely on a high-level understanding of calculus, probability and statistics, simple academic examples will be provided in support to the explanation of the theoretical foundations to facilitate comprehension of the subject matter. Case studies will be introduced to provide the practical value of the most advanced techniques. This detailed approach makes The Monte Carlo Simulation Method for System Reliability and Risk Analysis a key reference for senior undergraduate and graduate students as well as researchers and practitioners. It provides a powerful tool for all those involved in system analysis for reliability, maintenance and risk evaluations.
Author : Springer
Publisher :
ISBN 13 : 9781447145899
Total Pages : 214 pages
Book Rating : 4.1/5 (458 download)
Download or read book The Monte Carlo Simulation Method for System Reliability and Risk Analysis written by Springer and published by . This book was released on 2012-11-01 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Marzio Marseguerra
Publisher :
ISBN 13 : 9783934447066
Total Pages : 141 pages
Book Rating : 4.4/5 (47 download)
Download or read book Basics of the Monte Carlo Method with Application to System Reliability written by Marzio Marseguerra and published by . This book was released on 2002 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Yan Liu
Publisher : John Wiley & Sons
ISBN 13 : 1119287987
Total Pages : 265 pages
Book Rating : 4.1/5 (192 download)
Download or read book Practical Applications of Bayesian Reliability written by Yan Liu and published by John Wiley & Sons. This book was released on 2019-03-18 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: Demonstrates how to solve reliability problems using practical applications of Bayesian models This self-contained reference provides fundamental knowledge of Bayesian reliability and utilizes numerous examples to show how Bayesian models can solve real life reliability problems. It teaches engineers and scientists exactly what Bayesian analysis is, what its benefits are, and how they can apply the methods to solve their own problems. To help readers get started quickly, the book presents many Bayesian models that use JAGS and which require fewer than 10 lines of command. It also offers a number of short R scripts consisting of simple functions to help them become familiar with R coding. Practical Applications of Bayesian Reliability starts by introducing basic concepts of reliability engineering, including random variables, discrete and continuous probability distributions, hazard function, and censored data. Basic concepts of Bayesian statistics, models, reasons, and theory are presented in the following chapter. Coverage of Bayesian computation, Metropolis-Hastings algorithm, and Gibbs Sampling comes next. The book then goes on to teach the concepts of design capability and design for reliability; introduce Bayesian models for estimating system reliability; discuss Bayesian Hierarchical Models and their applications; present linear and logistic regression models in Bayesian Perspective; and more. Provides a step-by-step approach for developing advanced reliability models to solve complex problems, and does not require in-depth understanding of statistical methodology Educates managers on the potential of Bayesian reliability models and associated impact Introduces commonly used predictive reliability models and advanced Bayesian models based on real life applications Includes practical guidelines to construct Bayesian reliability models along with computer codes for all of the case studies JAGS and R codes are provided on an accompanying website to enable practitioners to easily copy them and tailor them to their own applications Practical Applications of Bayesian Reliability is a helpful book for industry practitioners such as reliability engineers, mechanical engineers, electrical engineers, product engineers, system engineers, and materials scientists whose work includes predicting design or product performance.
Author : Yu Hayakawa
Publisher : World Scientific
ISBN 13 : 9789812799548
Total Pages : 444 pages
Book Rating : 4.7/5 (995 download)
Download or read book System and Bayesian Reliability written by Yu Hayakawa and published by World Scientific. This book was released on 2001 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume is a collection of articles on reliability systems and Bayesian reliability analysis. Written by reputable researchers, the articles are self-contained and are linked with literature reviews and new research ideas. The book is dedicated to Emeritus Professor Richard E Barlow, who is well known for his pioneering research on reliability theory and Bayesian reliability analysis. Contents: System Reliability Analysis: On Regular Reliability Models (J-C Chang et al.); Bounding System Reliability (J N Hagstrom & S M Ross); Large Excesses for Finite-State Markov Chains (D Blackwell); Ageing Properties: Nonmonotonic Failure Rates and Mean Residual Life Functions (R C Gupta); The Failure Rate and the Mean Residual Lifetime of Mixtures (M S Finkelstein); On Some Discrete Notions of Aging (C Bracquemond et al.); Bayesian Analysis: On the Practical Implementation of the Bayesian Paradigm in Reliability and Risk Analysis (T Aven); A Weibull Wearout Test: Full Bayesian Approach (T Z Irony et al.); Bayesian Nonparametric Estimation of a Monotone Hazard Rate (M-W Ho & A Y Lo); and other papers. Readership: Students, academics, researchers and professionals in industrial engineering, probability and statistics, and applied mathematics.
Author : Dani Gamerman
Publisher : CRC Press
ISBN 13 : 9781584885870
Total Pages : 352 pages
Book Rating : 4.8/5 (858 download)
Download or read book Markov Chain Monte Carlo written by Dani Gamerman and published by CRC Press. This book was released on 2006-05-10 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: While there have been few theoretical contributions on the Markov Chain Monte Carlo (MCMC) methods in the past decade, current understanding and application of MCMC to the solution of inference problems has increased by leaps and bounds. Incorporating changes in theory and highlighting new applications, Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition presents a concise, accessible, and comprehensive introduction to the methods of this valuable simulation technique. The second edition includes access to an internet site that provides the code, written in R and WinBUGS, used in many of the previously existing and new examples and exercises. More importantly, the self-explanatory nature of the codes will enable modification of the inputs to the codes and variation on many directions will be available for further exploration. Major changes from the previous edition: · More examples with discussion of computational details in chapters on Gibbs sampling and Metropolis-Hastings algorithms · Recent developments in MCMC, including reversible jump, slice sampling, bridge sampling, path sampling, multiple-try, and delayed rejection · Discussion of computation using both R and WinBUGS · Additional exercises and selected solutions within the text, with all data sets and software available for download from the Web · Sections on spatial models and model adequacy The self-contained text units make MCMC accessible to scientists in other disciplines as well as statisticians. The book will appeal to everyone working with MCMC techniques, especially research and graduate statisticians and biostatisticians, and scientists handling data and formulating models. The book has been substantially reinforced as a first reading of material on MCMC and, consequently, as a textbook for modern Bayesian computation and Bayesian inference courses.
Author : Paolo Gardoni
Publisher : Springer
ISBN 13 : 3319524259
Total Pages : 558 pages
Book Rating : 4.3/5 (195 download)
Download or read book Risk and Reliability Analysis: Theory and Applications written by Paolo Gardoni and published by Springer. This book was released on 2017-02-24 with total page 558 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a unique collection of contributions from some of the foremost scholars in the field of risk and reliability analysis. Combining the most advanced analysis techniques with practical applications, it is one of the most comprehensive and up-to-date books available on risk-based engineering. All the fundamental concepts needed to conduct risk and reliability assessments are covered in detail, providing readers with a sound understanding of the field and making the book a powerful tool for students and researchers alike. This book was prepared in honor of Professor Armen Der Kiureghian, one of the fathers of modern risk and reliability analysis.
Author : Dani Gamerman
Publisher : CRC Press
ISBN 13 : 9780412818202
Total Pages : 264 pages
Book Rating : 4.8/5 (182 download)
Download or read book Markov Chain Monte Carlo written by Dani Gamerman and published by CRC Press. This book was released on 1997-10-01 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bridging the gap between research and application, Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference provides a concise, and integrated account of Markov chain Monte Carlo (MCMC) for performing Bayesian inference. This volume, which was developed from a short course taught by the author at a meeting of Brazilian statisticians and probabilists, retains the didactic character of the original course text. The self-contained text units make MCMC accessible to scientists in other disciplines as well as statisticians. It describes each component of the theory in detail and outlines related software, which is of particular benefit to applied scientists.
Author : Ming-Hui Chen
Publisher : Springer Science & Business Media
ISBN 13 : 1461212766
Total Pages : 399 pages
Book Rating : 4.4/5 (612 download)
Download or read book Monte Carlo Methods in Bayesian Computation written by Ming-Hui Chen and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dealing with methods for sampling from posterior distributions and how to compute posterior quantities of interest using Markov chain Monte Carlo (MCMC) samples, this book addresses such topics as improving simulation accuracy, marginal posterior density estimation, estimation of normalizing constants, constrained parameter problems, highest posterior density interval calculations, computation of posterior modes, and posterior computations for proportional hazards models and Dirichlet process models. The authors also discuss model comparisons, including both nested and non-nested models, marginal likelihood methods, ratios of normalizing constants, Bayes factors, the Savage-Dickey density ratio, Stochastic Search Variable Selection, Bayesian Model Averaging, the reverse jump algorithm, and model adequacy using predictive and latent residual approaches. The book presents an equal mixture of theory and applications involving real data, and is intended as a graduate textbook or a reference book for a one-semester course at the advanced masters or Ph.D. level. It will also serve as a useful reference for applied or theoretical researchers as well as practitioners.
Author : Naval Postgraduate School
Publisher :
ISBN 13 :
Total Pages : 198 pages
Book Rating : 4.5/5 (956 download)
Download or read book The Conceptual Design Reliability Prediction Method written by Naval Postgraduate School and published by . This book was released on 2021-01-16 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper presents a system reliability prediction method suitable for use during conceptual design, called the conceptual design reliability prediction method (CDRPM). The CDRPM extends the early design reliability prediction method (EDRPM) by facilitating parameter characterizations that follow non-normal distributions. Functional-physical reliability relationships are established through a hierarchical Bayesian model solved by Markov Chain Monte Carlo (MCMC) sampling and aggregated using the system's reliability block diagram (RBD) to assess the likelihood of candidate architectures meeting a reliability requirement. Reliability predictions based on different types of failure data, specifically success ratios and failure rates, are compared herein in a case study of a generic launcher system assessed by the CDRPM. This research shows the effects of failure data type selection and distribution assumptions on architecture down-selection, leading to enhanced insight during conceptual design analysis.
