Download or read book Solutions Manual for Uncertainty Modeling and Analysis in Engineering and the Sciences written by Bilal M. Ayyub and published by Chapman & Hall. This book was released on 2006-01 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Uncertainty Modeling and Analysis in Engineering and the Sciences written by Bilal M. Ayyub and published by CRC Press. This book was released on 2006-05-25 with total page 401 pages. Available in PDF, EPUB and Kindle. Book excerpt: Engineers and scientists often need to solve complex problems with incomplete information resources, necessitating a proper treatment of uncertainty and a reliance on expert opinions. Uncertainty Modeling and Analysis in Engineering and the Sciences prepares current and future analysts and practitioners to understand the fundamentals of knowledge a
Download or read book Uncertainty Analysis in Engineering and Sciences Fuzzy Logic Statistics and Neural Network Approach written by Bilal M. Ayyub and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertainty has been of concern to engineers, managers and . scientists for many centuries. In management sciences there have existed definitions of uncertainty in a rather narrow sense since the beginning of this century. In engineering and uncertainty has for a long time been considered as in sciences, however, synonymous with random, stochastic, statistic, or probabilistic. Only since the early sixties views on uncertainty have ~ecome more heterogeneous and more tools to model uncertainty than statistics have been proposed by several scientists. The problem of modeling uncertainty adequately has become more important the more complex systems have become, the faster the scientific and engineering world develops, and the more important, but also more difficult, forecasting of future states of systems have become. The first question one should probably ask is whether uncertainty is a phenomenon, a feature of real world systems, a state of mind or a label for a situation in which a human being wants to make statements about phenomena, i. e. , reality, models, and theories, respectively. One cart also ask whether uncertainty is an objective fact or just a subjective impression which is closely related to individual persons. Whether uncertainty is an objective feature of physical real systems seems to be a philosophical question. This shall not be answered in this volume.
Download or read book Uncertainty Analysis in Engineering and Sciences Fuzzy Logic Statistics and Neural Network Approach written by Bilal Ayyub and published by Springer. This book was released on 2011-09-28 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertainty has been of concern to engineers, managers and . scientists for many centuries. In management sciences there have existed definitions of uncertainty in a rather narrow sense since the beginning of this century. In engineering and uncertainty has for a long time been considered as in sciences, however, synonymous with random, stochastic, statistic, or probabilistic. Only since the early sixties views on uncertainty have ~ecome more heterogeneous and more tools to model uncertainty than statistics have been proposed by several scientists. The problem of modeling uncertainty adequately has become more important the more complex systems have become, the faster the scientific and engineering world develops, and the more important, but also more difficult, forecasting of future states of systems have become. The first question one should probably ask is whether uncertainty is a phenomenon, a feature of real world systems, a state of mind or a label for a situation in which a human being wants to make statements about phenomena, i. e. , reality, models, and theories, respectively. One cart also ask whether uncertainty is an objective fact or just a subjective impression which is closely related to individual persons. Whether uncertainty is an objective feature of physical real systems seems to be a philosophical question. This shall not be answered in this volume.
Download or read book Uncertainty Analysis for Engineers and Scientists written by Faith A. Morrison and published by Cambridge University Press. This book was released on 2021-01-07 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build the skills for determining appropriate error limits for quantities that matter with this essential toolkit. Understand how to handle a complete project and how uncertainty enters into various steps. Provides a systematic, worksheet-based process to determine error limits on measured quantities, and all likely sources of uncertainty are explored, measured or estimated. Features instructions on how to carry out error analysis using Excel and MATLAB®, making previously tedious calculations easy. Whether you are new to the sciences or an experienced engineer, this useful resource provides a practical approach to performing error analysis. Suitable as a text for a junior or senior level laboratory course in aerospace, chemical and mechanical engineering, and for professionals.
Download or read book Mathematics of Uncertainty Modeling in the Analysis of Engineering and Science Problems written by Snehashish Chakraverty and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book provides the reader with basic concepts for soft computing and other methods for various means of uncertainty in handling solutions, analysis, and applications"--
Download or read book Uncertainty Quantification and Predictive Computational Science written by Ryan G. McClarren and published by Springer. This book was released on 2018-11-23 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook teaches the essential background and skills for understanding and quantifying uncertainties in a computational simulation, and for predicting the behavior of a system under those uncertainties. It addresses a critical knowledge gap in the widespread adoption of simulation in high-consequence decision-making throughout the engineering and physical sciences. Constructing sophisticated techniques for prediction from basic building blocks, the book first reviews the fundamentals that underpin later topics of the book including probability, sampling, and Bayesian statistics. Part II focuses on applying Local Sensitivity Analysis to apportion uncertainty in the model outputs to sources of uncertainty in its inputs. Part III demonstrates techniques for quantifying the impact of parametric uncertainties on a problem, specifically how input uncertainties affect outputs. The final section covers techniques for applying uncertainty quantification to make predictions under uncertainty, including treatment of epistemic uncertainties. It presents the theory and practice of predicting the behavior of a system based on the aggregation of data from simulation, theory, and experiment. The text focuses on simulations based on the solution of systems of partial differential equations and includes in-depth coverage of Monte Carlo methods, basic design of computer experiments, as well as regularized statistical techniques. Code references, in python, appear throughout the text and online as executable code, enabling readers to perform the analysis under discussion. Worked examples from realistic, model problems help readers understand the mechanics of applying the methods. Each chapter ends with several assignable problems. Uncertainty Quantification and Predictive Computational Science fills the growing need for a classroom text for senior undergraduate and early-career graduate students in the engineering and physical sciences and supports independent study by researchers and professionals who must include uncertainty quantification and predictive science in the simulations they develop and/or perform.
Download or read book Mechanical Vibration written by Haym Benaroya and published by CRC Press. This book was released on 2004-10-13 with total page 746 pages. Available in PDF, EPUB and Kindle. Book excerpt: An effective text must be well balanced and thorough in its approach to a topic as expansive as vibration, and Mechanical Vibration is just such a textbook. Written for both senior undergraduate and graduate course levels, this updated and expanded second edition integrates uncertainty and control into the discussion of vibration, outlining basic concepts before delving into the mathematical rigors of modeling and analysis. Mechanical Vibration: Analysis, Uncertainties, and Control, Second Edition provides example problems, end-of-chapter exercises, and an up-to-date set of mini-projects to enhance students' computational abilities and includes abundant references for further study or more in-depth information. The author provides a MATLAB® primer on an accompanying CD-ROM, which contains original programs that can be used to solve complex problems and test solutions. The book is self-contained, covering both basic and more advanced topics such as stochastic processes and variational approaches. It concludes with a completely new chapter on nonlinear vibration and stability. Professors will find that the logical sequence of material is ideal for tailoring individualized syllabi, and students will benefit from the abundance of problems and MATLAB programs provided in the text and on the accompanying CD-ROM, respectively. A solutions manual is also available with qualifying course adoptions.
Download or read book Mathematics of Uncertainty Modeling in the Analysis of Engineering and Science Problems written by Snehashish Chakraverty and published by . This book was released on 2014 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Loss Models From Data to Decisions 4e Student Solutions Manual written by Stuart A. Klugman and published by John Wiley & Sons. This book was released on 2014-08-21 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Student Solutions Manual to Accompany Loss Models: From Data to Decisions, Fourth Edition. This volume is organised around the principle that much of actuarial science consists of the construction and analysis of mathematical models which describe the process by which funds flow into and out of an insurance system.
Download or read book Understanding Uncertainty written by Dennis V. Lindley and published by John Wiley & Sons. This book was released on 2013-12-16 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for the First Edition "...a reference for everyone who is interested in knowing and handling uncertainty." —Journal of Applied Statistics The critically acclaimed First Edition of Understanding Uncertainty provided a study of uncertainty addressed to scholars in all fields, showing that uncertainty could be measured by probability, and that probability obeyed three basic rules that enabled uncertainty to be handled sensibly in everyday life. These ideas were extended to embrace the scientific method and to show how decisions, containing an uncertain element, could be rationally made. Featuring new material, the Revised Edition remains the go-to guide for uncertainty and decision making, providing further applications at an accessible level including: A critical study of transitivity, a basic concept in probability A discussion of how the failure of the financial sector to use the proper approach to uncertainty may have contributed to the recent recession A consideration of betting, showing that a bookmaker's odds are not expressions of probability Applications of the book’s thesis to statistics A demonstration that some techniques currently popular in statistics, like significance tests, may be unsound, even seriously misleading, because they violate the rules of probability Understanding Uncertainty, Revised Edition is ideal for students studying probability or statistics and for anyone interested in one of the most fascinating and vibrant fields of study in contemporary science and mathematics.
Download or read book Measurement and Data Analysis for Engineering and Science written by Patrick F. Dunn and published by McGraw-Hill Education. This book was released on 2004-07-01 with total page 540 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Website includes solutions to all chapter problems, information on lab set-up and implementation, MATLAB resources and m-files, links to professional & association websites, and other student learning resources. Dunn's Experimental Methods & Measurement for Engineers offers an up-date-date, practical approach to learning and implementing lab skills, technical reporting, and data analysis. The first three chapters of the text cover the basics--experimental methods, units & significant figures, technical communications and basic electronics. Hardware issues are then presented, with a focus on measurement systems, and calibration & response. The final chapters deal with data analysis, with an overview of basic probability & statistics, uncertainty analysis, signal characteristics, and digital signal analysis. Following the text chapters, a full laboratory manual, with an introduction and twelve full lab experiments, is included. This gives users a chance to put their basic skills to work in actual engineering experiments, which are taken from a variety of engineering subject areas. Throughout the book computer techniques are discussed, and specific MATLAB applications are included, for problem modeling, exploration and solution. MATLAB "sidebars" are used to present MATLAB, and associated m-files are provided on the Book Website. This site will also include problem solutions and lab discussions for instructors, PowerPoint slides of key text figures, links to professional and association websites, and other student learning resources
Download or read book Fundamentals of Probability and Statistics for Engineers written by T. T. Soong and published by John Wiley and Sons. This book was released on 2004-03-26 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook differs from others in the field in that it has been prepared very much with students and their needs in mind, having been classroom tested over many years. It is a true “learner’s book” made for students who require a deeper understanding of probability and statistics. It presents the fundamentals of the subject along with concepts of probabilistic modelling, and the process of model selection, verification and analysis. Furthermore, the inclusion of more than 100 examples and 200 exercises (carefully selected from a wide range of topics), along with a solutions manual for instructors, means that this text is of real value to students and lecturers across a range of engineering disciplines. Key features: Presents the fundamentals in probability and statistics along with relevant applications. Explains the concept of probabilistic modelling and the process of model selection, verification and analysis. Definitions and theorems are carefully stated and topics rigorously treated. Includes a chapter on regression analysis. Covers design of experiments. Demonstrates practical problem solving throughout the book with numerous examples and exercises purposely selected from a variety of engineering fields. Includes an accompanying online Solutions Manual for instructors containing complete step-by-step solutions to all problems.
Download or read book Solutions Manual Modeling and Analysis of Dynamic Systems Second Edition written by Charles M. Close and published by Wiley. This book was released on 1994-12-09 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Geostatistics written by Jean-Paul Chilès and published by John Wiley & Sons. This book was released on 2012-03-26 with total page 750 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for the First Edition ". . . a readable, comprehensive volume that . . . belongs on the desk, close at hand, of any serious researcher or practitioner." —Mathematical Geosciences The state of the art in geostatistics Geostatistical models and techniques such as kriging and stochastic multi-realizations exploit spatial correlations to evaluate natural resources, help optimize their development, and address environmental issues related to air and water quality, soil pollution, and forestry. Geostatistics: Modeling Spatial Uncertainty, Second Edition presents a comprehensive, up-to-date reference on the topic, now featuring the latest developments in the field. The authors explain both the theory and applications of geostatistics through a unified treatment that emphasizes methodology. Key topics that are the foundation of geostatistics are explored in-depth, including stationary and nonstationary models; linear and nonlinear methods; change of support; multivariate approaches; and conditional simulations. The Second Edition highlights the growing number of applications of geostatistical methods and discusses three key areas of growth in the field: New results and methods, including kriging very large datasets; kriging with outliers; nonse??parable space-time covariances; multipoint simulations; pluri-gaussian simulations; gradual deformation; and extreme value geostatistics Newly formed connections between geostatistics and other approaches such as radial basis functions, Gaussian Markov random fields, and data assimilation New perspectives on topics such as collocated cokriging, kriging with an external drift, discrete Gaussian change-of-support models, and simulation algorithms Geostatistics, Second Edition is an excellent book for courses on the topic at the graduate level. It also serves as an invaluable reference for earth scientists, mining and petroleum engineers, geophysicists, and environmental statisticians who collect and analyze data in their everyday work.
Download or read book Introduction to Time Series Analysis and Forecasting written by Douglas C. Montgomery and published by John Wiley & Sons. This book was released on 2015-04-21 with total page 670 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for the First Edition "...[t]he book is great for readers who need to apply the methods and models presented but have little background in mathematics and statistics." -MAA Reviews Thoroughly updated throughout, Introduction to Time Series Analysis and Forecasting, Second Edition presents the underlying theories of time series analysis that are needed to analyze time-oriented data and construct real-world short- to medium-term statistical forecasts. Authored by highly-experienced academics and professionals in engineering statistics, the Second Edition features discussions on both popular and modern time series methodologies as well as an introduction to Bayesian methods in forecasting. Introduction to Time Series Analysis and Forecasting, Second Edition also includes: Over 300 exercises from diverse disciplines including health care, environmental studies, engineering, and finance More than 50 programming algorithms using JMP®, SAS®, and R that illustrate the theory and practicality of forecasting techniques in the context of time-oriented data New material on frequency domain and spatial temporal data analysis Expanded coverage of the variogram and spectrum with applications as well as transfer and intervention model functions A supplementary website featuring PowerPoint® slides, data sets, and select solutions to the problems Introduction to Time Series Analysis and Forecasting, Second Edition is an ideal textbook upper-undergraduate and graduate-levels courses in forecasting and time series. The book is also an excellent reference for practitioners and researchers who need to model and analyze time series data to generate forecasts.
Download or read book Bayesian Analysis of Stochastic Process Models written by David Insua and published by John Wiley & Sons. This book was released on 2012-05-07 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian analysis of complex models based on stochastic processes has in recent years become a growing area. This book provides a unified treatment of Bayesian analysis of models based on stochastic processes, covering the main classes of stochastic processing including modeling, computational, inference, forecasting, decision making and important applied models. Key features: Explores Bayesian analysis of models based on stochastic processes, providing a unified treatment. Provides a thorough introduction for research students. Computational tools to deal with complex problems are illustrated along with real life case studies Looks at inference, prediction and decision making. Researchers, graduate and advanced undergraduate students interested in stochastic processes in fields such as statistics, operations research (OR), engineering, finance, economics, computer science and Bayesian analysis will benefit from reading this book. With numerous applications included, practitioners of OR, stochastic modelling and applied statistics will also find this book useful.
