Mathematical Theory Of Reliability Of Time Dependent Systems With Practical Applications PDF Download
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| Author | : Igor N. Kovalenko |
| Publisher | : John Wiley & Sons |
| Total Pages | : 328 |
| Release | : 1997-07-16 |
| Genre | : Mathematics |
| ISBN | : |
Download Mathematical Theory of Reliability of Time Dependent Systems with Practical Applications Book in PDF, ePub and Kindle
One of the greatest problems in engineering is reliability. The performance of all machinery degrades over time and unless counteraction is taken at some point, any system will eventually fail. Once a system fails there are a number of possible solutions; the mathematical and statistical measurement and analysis of these solutions forms the mathematical theory of reliability. The aim of the authors is to concentrate on aspects of particular importance in the mathematical theory of reliability of time dependent systems rather than give a general overview. Particular emphasis is placed on fault tree analysis, Monte Carlo methods and importance measures. This book will be of particular interest to applied researchers and engineers working in areas where reliability is crucial. Contents Introduction, Markov and Semi-Markov models as a basis for the mathematical analysis of system reliability, methods for investigating homogeneous and non-homogeneous point processes (event flows), fault trees ? the current state of research, theory of redundant systems, Monte Carlo methods, reliability analysis using perturbation methods, stiff processes in reliability analysis, variance reduction methods, analytical-statistical methods for rapid simulation of repairable systems with structure redundancy, measures of reliability importance of components, index.
| Author | : Richard E. Barlow |
| Publisher | : SIAM |
| Total Pages | : 271 |
| Release | : 1996-01-01 |
| Genre | : Technology & Engineering |
| ISBN | : 0898713692 |
Download Mathematical Theory of Reliability Book in PDF, ePub and Kindle
This monograph presents a survey of mathematical models useful in solving reliability problems. It includes a detailed discussion of life distributions corresponding to wearout and their use in determining maintenance policies, and covers important topics such as the theory of increasing (decreasing) failure rate distributions, optimum maintenance policies, and the theory of coherent systems. The emphasis throughout the book is on making minimal assumptions - and only those based on plausible physical considerations - so that the resulting mathematical deductions may be safely made about a large variety of commonly occurring reliability situations. The first part of the book is concerned with component reliability, while the second part covers system reliability, including problems that are as important today as they were in the 1960s. The enduring relevance of the subject of reliability and the continuing demand for a graduate-level book on this topic are the driving forces behind its re-publication.
| Author | : Ngai Hang Chan |
| Publisher | : John Wiley & Sons |
| Total Pages | : 332 |
| Release | : 2011-01-25 |
| Genre | : Mathematics |
| ISBN | : 1118030710 |
Download Time Series Book in PDF, ePub and Kindle
A new edition of the comprehensive, hands-on guide to financial time series, now featuring S-Plus® and R software Time Series: Applications to Finance with R and S-Plus®, Second Edition is designed to present an in-depth introduction to the conceptual underpinnings and modern ideas of time series analysis. Utilizing interesting, real-world applications and the latest software packages, this book successfully helps readers grasp the technical and conceptual manner of the topic in order to gain a deeper understanding of the ever-changing dynamics of the financial world. With balanced coverage of both theory and applications, this Second Edition includes new content to accurately reflect the current state-of-the-art nature of financial time series analysis. A new chapter on Markov Chain Monte Carlo presents Bayesian methods for time series with coverage of Metropolis-Hastings algorithm, Gibbs sampling, and a case study that explores the relevance of these techniques for understanding activity in the Dow Jones Industrial Average. The author also supplies a new presentation of statistical arbitrage that includes discussion of pairs trading and cointegration. In addition to standard topics such as forecasting and spectral analysis, real-world financial examples are used to illustrate recent developments in nonstandard techniques, including: Nonstationarity Heteroscedasticity Multivariate time series State space modeling and stochastic volatility Multivariate GARCH Cointegration and common trends The book's succinct and focused organization allows readers to grasp the important ideas of time series. All examples are systematically illustrated with S-Plus® and R software, highlighting the relevance of time series in financial applications. End-of-chapter exercises and selected solutions allow readers to test their comprehension of the presented material, and a related Web site features additional data sets. Time Series: Applications to Finance with R and S-Plus® is an excellent book for courses on financial time series at the upper-undergraduate and beginning graduate levels. It also serves as an indispensible resource for practitioners working with financial data in the fields of statistics, economics, business, and risk management.
| Author | : Angela Mottaeva |
| Publisher | : Springer Nature |
| Total Pages | : 631 |
| Release | : 2020-12-23 |
| Genre | : Technology & Engineering |
| ISBN | : 9813362081 |
Download Proceedings of the XIII International Scientific Conference on Architecture and Construction 2020 Book in PDF, ePub and Kindle
The book contains the latest studies on digitalization of transport and logistics, improving vehicle fuel efficiency, information technology and digital security, land management and cadastres, building structures, structural analysis, and energy conservation in construction. This book consists of papers presented during the XIII International Scientific Conference on Architecture and Construction 2020, which is dedicated to the 90th anniversary of Novosibirsk State University of Architecture and Civil Engineering, held on September 22–24, 2020. The book caters to researchers, scientists and industrial practitioners in the field of transportation engineering, logistics, intelligent transport systems, sustainable construction for housing and industrial buildings.
| Author | : Andrew B. Lawson |
| Publisher | : John Wiley & Sons |
| Total Pages | : 302 |
| Release | : 2013-07-08 |
| Genre | : Medical |
| ISBN | : 1118723171 |
Download Statistical Methods in Spatial Epidemiology Book in PDF, ePub and Kindle
Spatial epidemiology is the description and analysis of the geographical distribution of disease. It is more important now than ever, with modern threats such as bio-terrorism making such analysis even more complex. This second edition of Statistical Methods in Spatial Epidemiology is updated and expanded to offer a complete coverage of the analysis and application of spatial statistical methods. The book is divided into two main sections: Part 1 introduces basic definitions and terminology, along with map construction and some basic models. This is expanded upon in Part II by applying this knowledge to the fundamental problems within spatial epidemiology, such as disease mapping, ecological analysis, disease clustering, bio-terrorism, space-time analysis, surveillance and infectious disease modelling. Provides a comprehensive overview of the main statistical methods used in spatial epidemiology. Updated to include a new emphasis on bio-terrorism and disease surveillance. Emphasizes the importance of space-time modelling and outlines the practical application of the method. Discusses the wide range of software available for analyzing spatial data, including WinBUGS, SaTScan and R, and features an accompanying website hosting related software. Contains numerous data sets, each representing a different approach to the analysis, and provides an insight into various modelling techniques. This text is primarily aimed at medical statisticians, researchers and practitioners from public health and epidemiology. It is also suitable for postgraduate students of statistics and epidemiology, as well professionals working in government agencies.
| Author | : Peter J. Huber |
| Publisher | : John Wiley & Sons |
| Total Pages | : 334 |
| Release | : 2004 |
| Genre | : Mathematics |
| ISBN | : 9780471650720 |
Download Robust Statistics Book in PDF, ePub and Kindle
The first systematic, book-length treatment of the subject. Begins with a general introduction and the formal mathematical background behind qualitative and quantitative robustness. Stresses concepts. Provides selected numerical algorithms for computing robust estimates, as well as convergence proofs. Tables contain quantitative robustness information for a variety of estimates.
| Author | : D. N. Prabhakar Murthy |
| Publisher | : John Wiley & Sons |
| Total Pages | : 409 |
| Release | : 2004-01-28 |
| Genre | : Mathematics |
| ISBN | : 0471473278 |
Download Weibull Models Book in PDF, ePub and Kindle
A comprehensive perspective on Weibull models The literature on Weibull models is vast, disjointed, andscattered across many different journals. Weibull Models is acomprehensive guide that integrates all the different facets ofWeibull models in a single volume. This book will be of great help to practitioners in reliabilityand other disciplines in the context of modeling data sets usingWeibull models. For researchers interested in these modelingtechniques, exercises at the end of each chapter define potentialtopics for future research. Organized into seven distinct parts, Weibull Models: * Covers model analysis, parameter estimation, model validation,and application * Serves as both a handbook and a research monograph. As ahandbook, it classifies the different models and presents theirproperties. As a research monograph, it unifies the literature andpresents the results in an integrated manner * Intertwines theory and application * Focuses on model identification prior to model parameterestimation * Discusses the usefulness of the Weibull Probability plot (WPP)in the model selection to model a given data set * Highlights the use of Weibull models in reliability theory Filled with in-depth analysis, Weibull Models pulls together themost relevant information on this topic to give everyone fromreliability engineers to applied statisticians involved withreliability and survival analysis a clear look at what Weibullmodels can offer.
| Author | : Xiao-Hua Zhou |
| Publisher | : John Wiley & Sons |
| Total Pages | : 597 |
| Release | : 2014-08-21 |
| Genre | : Medical |
| ISBN | : 1118626044 |
Download Statistical Methods in Diagnostic Medicine Book in PDF, ePub and Kindle
Praise for the First Edition " . . . the book is a valuable addition to the literature in the field, serving as a much-needed guide for both clinicians and advanced students."—Zentralblatt MATH A new edition of the cutting-edge guide to diagnostic tests in medical research In recent years, a considerable amount of research has focused on evolving methods for designing and analyzing diagnostic accuracy studies. Statistical Methods in Diagnostic Medicine, Second Edition continues to provide a comprehensive approach to the topic, guiding readers through the necessary practices for understanding these studies and generalizing the results to patient populations. Following a basic introduction to measuring test accuracy and study design, the authors successfully define various measures of diagnostic accuracy, describe strategies for designing diagnostic accuracy studies, and present key statistical methods for estimating and comparing test accuracy. Topics new to the Second Edition include: Methods for tests designed to detect and locate lesions Recommendations for covariate-adjustment Methods for estimating and comparing predictive values and sample size calculations Correcting techniques for verification and imperfect standard biases Sample size calculation for multiple reader studies when pilot data are available Updated meta-analysis methods, now incorporating random effects Three case studies thoroughly showcase some of the questions and statistical issues that arise in diagnostic medicine, with all associated data provided in detailed appendices. A related web site features Fortran, SAS®, and R software packages so that readers can conduct their own analyses. Statistical Methods in Diagnostic Medicine, Second Edition is an excellent supplement for biostatistics courses at the graduate level. It also serves as a valuable reference for clinicians and researchers working in the fields of medicine, epidemiology, and biostatistics.
| Author | : Dirk P. Kroese |
| Publisher | : John Wiley & Sons |
| Total Pages | : 627 |
| Release | : 2013-06-06 |
| Genre | : Mathematics |
| ISBN | : 1118014952 |
Download Handbook of Monte Carlo Methods Book in PDF, ePub and Kindle
A comprehensive overview of Monte Carlo simulation that explores the latest topics, techniques, and real-world applications More and more of today’s numerical problems found in engineering and finance are solved through Monte Carlo methods. The heightened popularity of these methods and their continuing development makes it important for researchers to have a comprehensive understanding of the Monte Carlo approach. Handbook of Monte Carlo Methods provides the theory, algorithms, and applications that helps provide a thorough understanding of the emerging dynamics of this rapidly-growing field. The authors begin with a discussion of fundamentals such as how to generate random numbers on a computer. Subsequent chapters discuss key Monte Carlo topics and methods, including: Random variable and stochastic process generation Markov chain Monte Carlo, featuring key algorithms such as the Metropolis-Hastings method, the Gibbs sampler, and hit-and-run Discrete-event simulation Techniques for the statistical analysis of simulation data including the delta method, steady-state estimation, and kernel density estimation Variance reduction, including importance sampling, latin hypercube sampling, and conditional Monte Carlo Estimation of derivatives and sensitivity analysis Advanced topics including cross-entropy, rare events, kernel density estimation, quasi Monte Carlo, particle systems, and randomized optimization The presented theoretical concepts are illustrated with worked examples that use MATLAB®, a related Web site houses the MATLAB® code, allowing readers to work hands-on with the material and also features the author's own lecture notes on Monte Carlo methods. Detailed appendices provide background material on probability theory, stochastic processes, and mathematical statistics as well as the key optimization concepts and techniques that are relevant to Monte Carlo simulation. Handbook of Monte Carlo Methods is an excellent reference for applied statisticians and practitioners working in the fields of engineering and finance who use or would like to learn how to use Monte Carlo in their research. It is also a suitable supplement for courses on Monte Carlo methods and computational statistics at the upper-undergraduate and graduate levels.
| Author | : Warren B. Powell |
| Publisher | : John Wiley & Sons |
| Total Pages | : 416 |
| Release | : 2013-07-09 |
| Genre | : Mathematics |
| ISBN | : 1118309847 |
Download Optimal Learning Book in PDF, ePub and Kindle
Learn the science of collecting information to make effective decisions Everyday decisions are made without the benefit of accurate information. Optimal Learning develops the needed principles for gathering information to make decisions, especially when collecting information is time-consuming and expensive. Designed for readers with an elementary background in probability and statistics, the book presents effective and practical policies illustrated in a wide range of applications, from energy, homeland security, and transportation to engineering, health, and business. This book covers the fundamental dimensions of a learning problem and presents a simple method for testing and comparing policies for learning. Special attention is given to the knowledge gradient policy and its use with a wide range of belief models, including lookup table and parametric and for online and offline problems. Three sections develop ideas with increasing levels of sophistication: Fundamentals explores fundamental topics, including adaptive learning, ranking and selection, the knowledge gradient, and bandit problems Extensions and Applications features coverage of linear belief models, subset selection models, scalar function optimization, optimal bidding, and stopping problems Advanced Topics explores complex methods including simulation optimization, active learning in mathematical programming, and optimal continuous measurements Each chapter identifies a specific learning problem, presents the related, practical algorithms for implementation, and concludes with numerous exercises. A related website features additional applications and downloadable software, including MATLAB and the Optimal Learning Calculator, a spreadsheet-based package that provides an introduction to learning and a variety of policies for learning.
