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Título : Reliability, Life Testing and the Prediction of Service Lives : For Engineers and Scientists Tipo de documento: documento electrónico Autores: Saunders, Sam C ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2007 Colección: Springer Series in Statistics, ISSN 0172-7397 Número de páginas: XIV, 308 p Il.: online resource ISBN/ISSN/DL: 978-0-387-48538-6 Idioma : Inglés (eng) Palabras clave: Computer science software Reusability Probabilities Statistics Engineering Applied mathematics Quality control Reliability Industrial safety Science Performance and Engineering, general Appl.Mathematics/Computational Methods of Control, Reliability, Safety Risk for Physics, Science, Chemistry Earth Sciences Probability Theory Stochastic Processes Clasificación: 51 Matemáticas Resumen: This book is intended for students and practitioners who have had a calculus-based statistics course and who have an interest in safety considerations such as reliability, strength, and duration-of-load or service life. Many persons studying statistical science will be employed professionally where the problems encountered are obscure, what should be analyzed is not clear, the appropriate assumptions are equivocal, and data are scant. Yet tutorial problems of this nature are virtually never encountered in coursework. In this book there is no disclosure with many of the data sets what type of investigation should be made or what assumptions are to be used. Most reliability practitioners will be employed where personal interaction between disciplines is a necessity. A section is included on communication skills to facilitate model selection and formulation based on verifiable assumptions, rather than favorable conclusions. However, whether the answer is "right" can never be ascertained. Past and current applications of stochastic modeling to life-length can only be a guide for future adaptations under different conditions, with new materials in unknown usages. This book unifies the study of cumulative-damage distributions, namely, Wald and Tweedie (i.e., inverse-Gaussian and its reciprocal) with "fatigue-life." These distributions are most useful when the coefficient-of-variation is more appropriate than is the variance as a measure of dispersion. It is shown, uniquely, that the same hyperbolic-sine transformation of each life length variate has a Chi-square one-df distribution. This property is useful in the sample statistics. These IHRA distributions realistically model life-length, strength or duration of load under linear cumulative damage and can be combined as approximations in non-linear situations. Sam C. Saunders has served as a research engineer for 17 years at the Boeing Scientific Research Laboratories, 20 years as a consultant to the Advisory Committee for Nuclear Safeguards, 10 years as a consultant to NIST, was a principal in the consulting firms Mathematical Analysis Research Corporation and Scientific Consulting Service; and was for 26 years a professor of Applied Mathematics/Statistics at Washington State University. He is a Fellow of the American Statistical Association and a former editor of Technometrics Nota de contenido: Requisites -- Elements of Reliability -- Partitions and Selection -- Coherent Systems -- Applicable Life Distributions -- Philosophy, Science, and Sense -- Nonparametric Life Estimators -- Weibull Analysis -- Examine Data, Diagnose and Consult -- Cumulative Damage Distributions -- Analysis of Dispersion -- Damage Processes -- Service Life of Structures -- Strength and Durability -- Maintenance of Systems -- Mathematical Appendix En línea: http://dx.doi.org/10.1007/978-0-387-48538-6 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34476 Reliability, Life Testing and the Prediction of Service Lives : For Engineers and Scientists [documento electrónico] / Saunders, Sam C ; SpringerLink (Online service) . - New York, NY : Springer New York, 2007 . - XIV, 308 p : online resource. - (Springer Series in Statistics, ISSN 0172-7397) .
ISBN : 978-0-387-48538-6
Idioma : Inglés (eng)
Palabras clave: Computer science software Reusability Probabilities Statistics Engineering Applied mathematics Quality control Reliability Industrial safety Science Performance and Engineering, general Appl.Mathematics/Computational Methods of Control, Reliability, Safety Risk for Physics, Science, Chemistry Earth Sciences Probability Theory Stochastic Processes Clasificación: 51 Matemáticas Resumen: This book is intended for students and practitioners who have had a calculus-based statistics course and who have an interest in safety considerations such as reliability, strength, and duration-of-load or service life. Many persons studying statistical science will be employed professionally where the problems encountered are obscure, what should be analyzed is not clear, the appropriate assumptions are equivocal, and data are scant. Yet tutorial problems of this nature are virtually never encountered in coursework. In this book there is no disclosure with many of the data sets what type of investigation should be made or what assumptions are to be used. Most reliability practitioners will be employed where personal interaction between disciplines is a necessity. A section is included on communication skills to facilitate model selection and formulation based on verifiable assumptions, rather than favorable conclusions. However, whether the answer is "right" can never be ascertained. Past and current applications of stochastic modeling to life-length can only be a guide for future adaptations under different conditions, with new materials in unknown usages. This book unifies the study of cumulative-damage distributions, namely, Wald and Tweedie (i.e., inverse-Gaussian and its reciprocal) with "fatigue-life." These distributions are most useful when the coefficient-of-variation is more appropriate than is the variance as a measure of dispersion. It is shown, uniquely, that the same hyperbolic-sine transformation of each life length variate has a Chi-square one-df distribution. This property is useful in the sample statistics. These IHRA distributions realistically model life-length, strength or duration of load under linear cumulative damage and can be combined as approximations in non-linear situations. Sam C. Saunders has served as a research engineer for 17 years at the Boeing Scientific Research Laboratories, 20 years as a consultant to the Advisory Committee for Nuclear Safeguards, 10 years as a consultant to NIST, was a principal in the consulting firms Mathematical Analysis Research Corporation and Scientific Consulting Service; and was for 26 years a professor of Applied Mathematics/Statistics at Washington State University. He is a Fellow of the American Statistical Association and a former editor of Technometrics Nota de contenido: Requisites -- Elements of Reliability -- Partitions and Selection -- Coherent Systems -- Applicable Life Distributions -- Philosophy, Science, and Sense -- Nonparametric Life Estimators -- Weibull Analysis -- Examine Data, Diagnose and Consult -- Cumulative Damage Distributions -- Analysis of Dispersion -- Damage Processes -- Service Life of Structures -- Strength and Durability -- Maintenance of Systems -- Mathematical Appendix En línea: http://dx.doi.org/10.1007/978-0-387-48538-6 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34476 Ejemplares
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Título : Stochastic Models in Reliability Tipo de documento: documento electrónico Autores: Terje Aven ; SpringerLink (Online service) ; Jensen, Uwe Editorial: New York, NY : Springer New York Fecha de publicación: 2013 Otro editor: Imprint: Springer Colección: Stochastic Modelling and Applied Probability, ISSN 0172-4568 num. 41 Número de páginas: XIV, 297 p Il.: online resource ISBN/ISSN/DL: 978-1-4614-7894-2 Idioma : Inglés (eng) Palabras clave: Mathematics Operations research Management science Probabilities Quality control Reliability Industrial safety Probability Theory and Stochastic Processes Control, Reliability, Safety Risk Research, Science Clasificación: 51 Matemáticas Resumen: This book provides a comprehensive up-to-date presentation of some of the classical areas of reliability, based on a more advanced probabilistic framework using the modern theory of stochastic processes. This framework allows analysts to formulate general failure models, establish formulae for computing various performance measures, as well as determine how to identify optimal replacement policies in complex situations. In this second edition of the book, two major topics have been added to the original version: copula models which are used to study the effect of structural dependencies on the system reliability; and maintenance optimization which highlights delay time models under safety constraints. Terje Aven is Professor of Reliability and Risk Analysis at University of Stavanger, Norway. Uwe Jensen is working as a Professor at the Institute of Applied Mathematics and Statistics of the University of Hohenheim in Stuttgart, Germany. Review of first edition: "This is an excellent book on mathematical, statistical and stochastic models in reliability. The authors have done an excellent job of unifying some of the stochastic models in reliability. The book is a good reference book but may not be suitable as a textbook for students in professional fields such as engineering. This book may be used for graduate level seminar courses for students who have had at least the first course in stochastic processes and some knowledge of reliability mathematics. It should be a good reference book for researchers in reliability mathematics." Mathematical Reviews (2000) Nota de contenido: Introduction -- Basic Reliability Theory -- Stochastic Failure Models -- Availability Analysis of Complex Systems -- Maintenance Optimization En línea: http://dx.doi.org/10.1007/978-1-4614-7894-2 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32371 Stochastic Models in Reliability [documento electrónico] / Terje Aven ; SpringerLink (Online service) ; Jensen, Uwe . - New York, NY : Springer New York : Imprint: Springer, 2013 . - XIV, 297 p : online resource. - (Stochastic Modelling and Applied Probability, ISSN 0172-4568; 41) .
ISBN : 978-1-4614-7894-2
Idioma : Inglés (eng)
Palabras clave: Mathematics Operations research Management science Probabilities Quality control Reliability Industrial safety Probability Theory and Stochastic Processes Control, Reliability, Safety Risk Research, Science Clasificación: 51 Matemáticas Resumen: This book provides a comprehensive up-to-date presentation of some of the classical areas of reliability, based on a more advanced probabilistic framework using the modern theory of stochastic processes. This framework allows analysts to formulate general failure models, establish formulae for computing various performance measures, as well as determine how to identify optimal replacement policies in complex situations. In this second edition of the book, two major topics have been added to the original version: copula models which are used to study the effect of structural dependencies on the system reliability; and maintenance optimization which highlights delay time models under safety constraints. Terje Aven is Professor of Reliability and Risk Analysis at University of Stavanger, Norway. Uwe Jensen is working as a Professor at the Institute of Applied Mathematics and Statistics of the University of Hohenheim in Stuttgart, Germany. Review of first edition: "This is an excellent book on mathematical, statistical and stochastic models in reliability. The authors have done an excellent job of unifying some of the stochastic models in reliability. The book is a good reference book but may not be suitable as a textbook for students in professional fields such as engineering. This book may be used for graduate level seminar courses for students who have had at least the first course in stochastic processes and some knowledge of reliability mathematics. It should be a good reference book for researchers in reliability mathematics." Mathematical Reviews (2000) Nota de contenido: Introduction -- Basic Reliability Theory -- Stochastic Failure Models -- Availability Analysis of Complex Systems -- Maintenance Optimization En línea: http://dx.doi.org/10.1007/978-1-4614-7894-2 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32371 Ejemplares
Signatura Medio Ubicación Sub-localización Sección Estado ningún ejemplar Mathematical and Statistical Models and Methods in Reliability / SpringerLink (Online service) ; Rykov, V.V ; Nagraj Balakrishnan ; Nikulin, M.S (2010)
Título : Mathematical and Statistical Models and Methods in Reliability : Applications to Medicine, Finance, and Quality Control Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Rykov, V.V ; Nagraj Balakrishnan ; Nikulin, M.S Editorial: Boston, MA : Birkhäuser Boston Fecha de publicación: 2010 Otro editor: Imprint: Birkhäuser Colección: Statistics for Industry and Technology, ISSN 2364-6241 Número de páginas: XXVI, 457 p. 74 illus Il.: online resource ISBN/ISSN/DL: 978-0-8176-4971-5 Idioma : Inglés (eng) Palabras clave: Engineering Applied mathematics Mathematical models Probabilities Statistics Quality control Reliability Industrial safety Control, Reliability, Safety and Risk Statistical Theory Methods Applications of Mathematics for Life Sciences, Medicine, Health Sciences Probability Stochastic Processes Modeling Clasificación: 51 Matemáticas Resumen: An outgrowth of the sixth conference on “Mathematical Methods in Reliability: Theory, Methods, and Applications,” this book is a selection of invited chapters, all of which deal with various aspects of mathematical and statistical models and methods in reliability. Written by recognized experts in the field of reliability, the contributions cover a wide range of models, methods, and applications, reflecting recent developments in areas such as survival analysis, aging, lifetime data analysis, artificial intelligence, medicine, carcinogenesis studies, nuclear power, financial modeling, aircraft engineering, quality control, and transportation. The volume is thematically organized into four major sections: * Reliability Models, Methods, and Optimization; * Statistical Methods in Reliability; * Applications; * Computer Tools for Reliability. Mathematical and Statistical Models and Methods in Reliability is an excellent reference text for researchers and practitioners in applied probability and statistics, industrial statistics, engineering, medicine, finance, transportation, the oil and gas industry, and artificial intelligence Nota de contenido: Reliability Models, Methods, and Optimization -- Reliability of Semi-Markov Systems with Asymptotic Merging Phase Space -- Nonlinearly Perturbed Stochastic Processes and Systems -- On a Copula for Failure Times of System Elements -- On One Method of Reliability Coefficients Calculation for Objects in Non-Homogeneous Event Flows -- A New Approach to Maintenance Optimization by Modeling Intensity Control -- Longitudinal Latent Markov Processes Observable Through an Invariant Rasch Model -- Dynamics of Dependence Properties for Lifetimes Influenced by Unobservable Environmental Factors -- On Alternative of Choice for a Prophylaxis Problem -- Optimal Incomplete Maintenance for Systems with Discrete Time-to-Failure Distribution -- A Gini-Type Index for Aging/Rejuvenating Objects -- Redundancy Analysis for Multi-state System: Reliability and Financial Assessment -- On the Reliability Modeling of Hierarchical Systems -- Statistical Methods in Reliability -- Parametric Estimation of Redundant System Reliability From Censored Data -- Assessing Accuracy of Statistical Inferences by Resamplings -- Change Point Estimation in Regression Models with Fixed Design -- A Model for Field Failure Prediction Using Dynamic Environmental Data -- Efficient Regression Estimation Under General Censoring and Truncation -- On Generalized Tests of Fit for Multinomial Populations -- Modeling and Scaling of Categorical Data -- Nonparametric Estimation and Testing the Effect of Covariates in Accelerated Life Time Models Under Censoring -- Nonparametric Estimation of Time Trend for Repairable Systems Data -- Confidence Region for Distribution Function from Censored Data -- Empirical Estimate with Uniformly Minimal d-Risk for Bernoulli Trials Success Probability -- Estimation of Archival Lifetime Distribution for Writable Optical Disks from Accelerated Testings -- Applications -- Ages in Reliability and Bio Systems, Interpretations, Control, and Applications -- Shocks in Mixed Populations -- Bayesian Estimation of Degradation Model Defined by a Wiener Process -- Benefits of Threshold Regression: A Case-Study Comparison with Cox Proportional Hazards Regression -- Optimal Stopping and Reselling of European Options -- Bayesian Modeling of Health State Preferences -- Information Measures in Biostatistics and Reliability Engineering -- Reliability Computer Tools -- Software System for Simulation and Research of Probabilistic Regularities and Statistical Data Analysis in Reliability and Quality Control -- Inverse Gaussian Model and Its Applications in Reliability and Survival Analysis En línea: http://dx.doi.org/10.1007/978-0-8176-4971-5 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33562 Mathematical and Statistical Models and Methods in Reliability : Applications to Medicine, Finance, and Quality Control [documento electrónico] / SpringerLink (Online service) ; Rykov, V.V ; Nagraj Balakrishnan ; Nikulin, M.S . - Boston, MA : Birkhäuser Boston : Imprint: Birkhäuser, 2010 . - XXVI, 457 p. 74 illus : online resource. - (Statistics for Industry and Technology, ISSN 2364-6241) .
ISBN : 978-0-8176-4971-5
Idioma : Inglés (eng)
Palabras clave: Engineering Applied mathematics Mathematical models Probabilities Statistics Quality control Reliability Industrial safety Control, Reliability, Safety and Risk Statistical Theory Methods Applications of Mathematics for Life Sciences, Medicine, Health Sciences Probability Stochastic Processes Modeling Clasificación: 51 Matemáticas Resumen: An outgrowth of the sixth conference on “Mathematical Methods in Reliability: Theory, Methods, and Applications,” this book is a selection of invited chapters, all of which deal with various aspects of mathematical and statistical models and methods in reliability. Written by recognized experts in the field of reliability, the contributions cover a wide range of models, methods, and applications, reflecting recent developments in areas such as survival analysis, aging, lifetime data analysis, artificial intelligence, medicine, carcinogenesis studies, nuclear power, financial modeling, aircraft engineering, quality control, and transportation. The volume is thematically organized into four major sections: * Reliability Models, Methods, and Optimization; * Statistical Methods in Reliability; * Applications; * Computer Tools for Reliability. Mathematical and Statistical Models and Methods in Reliability is an excellent reference text for researchers and practitioners in applied probability and statistics, industrial statistics, engineering, medicine, finance, transportation, the oil and gas industry, and artificial intelligence Nota de contenido: Reliability Models, Methods, and Optimization -- Reliability of Semi-Markov Systems with Asymptotic Merging Phase Space -- Nonlinearly Perturbed Stochastic Processes and Systems -- On a Copula for Failure Times of System Elements -- On One Method of Reliability Coefficients Calculation for Objects in Non-Homogeneous Event Flows -- A New Approach to Maintenance Optimization by Modeling Intensity Control -- Longitudinal Latent Markov Processes Observable Through an Invariant Rasch Model -- Dynamics of Dependence Properties for Lifetimes Influenced by Unobservable Environmental Factors -- On Alternative of Choice for a Prophylaxis Problem -- Optimal Incomplete Maintenance for Systems with Discrete Time-to-Failure Distribution -- A Gini-Type Index for Aging/Rejuvenating Objects -- Redundancy Analysis for Multi-state System: Reliability and Financial Assessment -- On the Reliability Modeling of Hierarchical Systems -- Statistical Methods in Reliability -- Parametric Estimation of Redundant System Reliability From Censored Data -- Assessing Accuracy of Statistical Inferences by Resamplings -- Change Point Estimation in Regression Models with Fixed Design -- A Model for Field Failure Prediction Using Dynamic Environmental Data -- Efficient Regression Estimation Under General Censoring and Truncation -- On Generalized Tests of Fit for Multinomial Populations -- Modeling and Scaling of Categorical Data -- Nonparametric Estimation and Testing the Effect of Covariates in Accelerated Life Time Models Under Censoring -- Nonparametric Estimation of Time Trend for Repairable Systems Data -- Confidence Region for Distribution Function from Censored Data -- Empirical Estimate with Uniformly Minimal d-Risk for Bernoulli Trials Success Probability -- Estimation of Archival Lifetime Distribution for Writable Optical Disks from Accelerated Testings -- Applications -- Ages in Reliability and Bio Systems, Interpretations, Control, and Applications -- Shocks in Mixed Populations -- Bayesian Estimation of Degradation Model Defined by a Wiener Process -- Benefits of Threshold Regression: A Case-Study Comparison with Cox Proportional Hazards Regression -- Optimal Stopping and Reselling of European Options -- Bayesian Modeling of Health State Preferences -- Information Measures in Biostatistics and Reliability Engineering -- Reliability Computer Tools -- Software System for Simulation and Research of Probabilistic Regularities and Statistical Data Analysis in Reliability and Quality Control -- Inverse Gaussian Model and Its Applications in Reliability and Survival Analysis En línea: http://dx.doi.org/10.1007/978-0-8176-4971-5 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33562 Ejemplares
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Título : Semi-Markov Chains and Hidden Semi-Markov Models toward Applications : Their use in Reliability and DNA Analysis Tipo de documento: documento electrónico Autores: Limnios, Nikolaos ; SpringerLink (Online service) ; Vlad Stefan Barbu Editorial: New York, NY : Springer New York Fecha de publicación: 2008 Colección: Lecture Notes in Statistics, ISSN 0930-0325 num. 191 Número de páginas: XIV, 226 p Il.: online resource ISBN/ISSN/DL: 978-0-387-73173-5 Idioma : Inglés (eng) Palabras clave: Mathematics Bioinformatics Operations research Management science Probabilities Statistics Quality control Reliability Industrial safety Probability Theory and Stochastic Processes Statistical Methods for Engineering, Physics, Computer Science, Chemistry Earth Sciences Control, Reliability, Safety Risk Research, Science Clasificación: 51 Matemáticas Resumen: This book is concerned with the estimation of discrete-time semi-Markov and hidden semi-Markov processes. Semi-Markov processes are much more general and better adapted to applications than the Markov ones because sojourn times in any state can be arbitrarily distributed, as opposed to the geometrically distributed sojourn time in the Markov case. Another unique feature of the book is the use of discrete time, especially useful in some specific applications where the time scale is intrinsically discrete. The models presented in the book are specifically adapted to reliability studies and DNA analysis. The book is mainly intended for applied probabilists and statisticians interested in semi-Markov chains theory, reliability and DNA analysis, and for theoretical oriented reliability and bioinformatics engineers. It can also serve as a text for a six month research-oriented course at a Master or PhD level. The prerequisites are a background in probability theory and finite state space Markov chains. Vlad Stefan Barbu is associate professor in statistics at the University of Rouen, France, Laboratory of Mathematics ‘Raphaël Salem.’ His research focuses basically on stochastic processes and associated statistical problems, with a particular interest in reliability and DNA analysis. He has published several papers in the field. Nikolaos Limnios is a professor in Applied Mathematics at the University of Technology of Compiègne. His research interest concerns stochastic processes and statistics with application to reliability. He is the co-author of the books: Semi-Markov Processes and Reliability (Birkhäuser, 2001 with G. Oprisan) and Stochastic Systems in Merging Phase Space (World Scientific, 2005, with V.S. Koroliuk) Nota de contenido: Discrete-Time Renewal Processes -- Semi-Markov Chains -- Non parametric Estimation for Semi-Markov Chains -- Reliability Theory for Discrete-Time Semi-Markov Systems -- Hidden Semi-Markov Model and Estimation En línea: http://dx.doi.org/10.1007/978-0-387-73173-5 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34175 Semi-Markov Chains and Hidden Semi-Markov Models toward Applications : Their use in Reliability and DNA Analysis [documento electrónico] / Limnios, Nikolaos ; SpringerLink (Online service) ; Vlad Stefan Barbu . - New York, NY : Springer New York, 2008 . - XIV, 226 p : online resource. - (Lecture Notes in Statistics, ISSN 0930-0325; 191) .
ISBN : 978-0-387-73173-5
Idioma : Inglés (eng)
Palabras clave: Mathematics Bioinformatics Operations research Management science Probabilities Statistics Quality control Reliability Industrial safety Probability Theory and Stochastic Processes Statistical Methods for Engineering, Physics, Computer Science, Chemistry Earth Sciences Control, Reliability, Safety Risk Research, Science Clasificación: 51 Matemáticas Resumen: This book is concerned with the estimation of discrete-time semi-Markov and hidden semi-Markov processes. Semi-Markov processes are much more general and better adapted to applications than the Markov ones because sojourn times in any state can be arbitrarily distributed, as opposed to the geometrically distributed sojourn time in the Markov case. Another unique feature of the book is the use of discrete time, especially useful in some specific applications where the time scale is intrinsically discrete. The models presented in the book are specifically adapted to reliability studies and DNA analysis. The book is mainly intended for applied probabilists and statisticians interested in semi-Markov chains theory, reliability and DNA analysis, and for theoretical oriented reliability and bioinformatics engineers. It can also serve as a text for a six month research-oriented course at a Master or PhD level. The prerequisites are a background in probability theory and finite state space Markov chains. Vlad Stefan Barbu is associate professor in statistics at the University of Rouen, France, Laboratory of Mathematics ‘Raphaël Salem.’ His research focuses basically on stochastic processes and associated statistical problems, with a particular interest in reliability and DNA analysis. He has published several papers in the field. Nikolaos Limnios is a professor in Applied Mathematics at the University of Technology of Compiègne. His research interest concerns stochastic processes and statistics with application to reliability. He is the co-author of the books: Semi-Markov Processes and Reliability (Birkhäuser, 2001 with G. Oprisan) and Stochastic Systems in Merging Phase Space (World Scientific, 2005, with V.S. Koroliuk) Nota de contenido: Discrete-Time Renewal Processes -- Semi-Markov Chains -- Non parametric Estimation for Semi-Markov Chains -- Reliability Theory for Discrete-Time Semi-Markov Systems -- Hidden Semi-Markov Model and Estimation En línea: http://dx.doi.org/10.1007/978-0-387-73173-5 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34175 Ejemplares
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Título : Bayesian Reliability Tipo de documento: documento electrónico Autores: Hamada, Michael S ; SpringerLink (Online service) ; Wilson, Alyson G ; Reese, C. Shane ; Martz, Harry F Editorial: New York, NY : Springer New York Fecha de publicación: 2008 Colección: Springer Series in Statistics, ISSN 0172-7397 Número de páginas: XVI, 436 p Il.: online resource ISBN/ISSN/DL: 978-0-387-77950-8 Idioma : Inglés (eng) Palabras clave: Mathematics Probabilities Statistics Quality control Reliability Industrial safety Probability Theory and Stochastic Processes for Engineering, Physics, Computer Science, Chemistry Earth Sciences Control, Reliability, Safety Risk Statistical Methods Clasificación: 51 Matemáticas Resumen: 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 Nota de contenido: Reliability Concepts -- Bayesian Inference -- Advanced Bayesian Modeling and Computational Methods -- Component Reliability -- System Reliability -- Repairable System Reliability -- Regression Models in Reliability -- Using Degradation Data to Assess Reliability -- Planning for Reliability Data Collection -- Assurance Testing En línea: http://dx.doi.org/10.1007/978-0-387-77950-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34240 Bayesian Reliability [documento electrónico] / Hamada, Michael S ; SpringerLink (Online service) ; Wilson, Alyson G ; Reese, C. Shane ; Martz, Harry F . - New York, NY : Springer New York, 2008 . - XVI, 436 p : online resource. - (Springer Series in Statistics, ISSN 0172-7397) .
ISBN : 978-0-387-77950-8
Idioma : Inglés (eng)
Palabras clave: Mathematics Probabilities Statistics Quality control Reliability Industrial safety Probability Theory and Stochastic Processes for Engineering, Physics, Computer Science, Chemistry Earth Sciences Control, Reliability, Safety Risk Statistical Methods Clasificación: 51 Matemáticas Resumen: 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 Nota de contenido: Reliability Concepts -- Bayesian Inference -- Advanced Bayesian Modeling and Computational Methods -- Component Reliability -- System Reliability -- Repairable System Reliability -- Regression Models in Reliability -- Using Degradation Data to Assess Reliability -- Planning for Reliability Data Collection -- Assurance Testing En línea: http://dx.doi.org/10.1007/978-0-387-77950-8 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34240 Ejemplares
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