Modeling Change And Uncertainty

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Uncertainty Modeling for Engineering Applications

Author : Flavio Canavero
Publisher : Springer
ISBN 13 : 9783030048693
Total Pages : 0 pages
Book Rating : 4.0/5 (486 download)

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Book Synopsis Uncertainty Modeling for Engineering Applications by : Flavio Canavero

Download or read book Uncertainty Modeling for Engineering Applications written by Flavio Canavero and published by Springer. This book was released on 2019-01-16 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an overview of state-of-the-art uncertainty quantification (UQ) methodologies and applications, and covers a wide range of current research, future challenges and applications in various domains, such as aerospace and mechanical applications, structure health and seismic hazard, electromagnetic energy (its impact on systems and humans) and global environmental state change. Written by leading international experts from different fields, the book demonstrates the unifying property of UQ theme that can be profitably adopted to solve problems of different domains. The collection in one place of different methodologies for different applications has the great value of stimulating the cross-fertilization and alleviate the language barrier among areas sharing a common background of mathematical modeling for problem solution. The book is designed for researchers, professionals and graduate students interested in quantitatively assessing the effects of uncertainties in their fields of application. The contents build upon the workshop “Uncertainty Modeling for Engineering Applications” (UMEMA 2017), held in Torino, Italy in November 2017.

Modeling Change and Uncertainty

Author : William P. Fox
Publisher : CRC Press
ISBN 13 : 1000603873
Total Pages : 465 pages
Book Rating : 4.0/5 (6 download)

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Book Synopsis Modeling Change and Uncertainty by : William P. Fox

Download or read book Modeling Change and Uncertainty written by William P. Fox and published by CRC Press. This book was released on 2022-07-20 with total page 465 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a problem-solving approach. The authors introduce a problem to help motivate the learning of a particular mathematical modeling topic. The problem provides the issue or what is needed to solve using an appropriate modeling technique.

Modeling and Inverse Problems in the Presence of Uncertainty

Author : H. T. Banks
Publisher : CRC Press
ISBN 13 : 1482206439
Total Pages : 403 pages
Book Rating : 4.4/5 (822 download)

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Book Synopsis Modeling and Inverse Problems in the Presence of Uncertainty by : H. T. Banks

Download or read book Modeling and Inverse Problems in the Presence of Uncertainty written by H. T. Banks and published by CRC Press. This book was released on 2014-04-01 with total page 403 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling and Inverse Problems in the Presence of Uncertainty collects recent research-including the authors' own substantial projects-on uncertainty propagation and quantification. It covers two sources of uncertainty: where uncertainty is present primarily due to measurement errors and where uncertainty is present due to the modeling formulation i

Modeling Uncertainty

Author : Moshe Dror
Publisher : Springer
ISBN 13 : 9781475783698
Total Pages : 770 pages
Book Rating : 4.7/5 (836 download)

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Book Synopsis Modeling Uncertainty by : Moshe Dror

Download or read book Modeling Uncertainty written by Moshe Dror and published by Springer. This book was released on 2014-03-24 with total page 770 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling Uncertainty: An Examination of Stochastic Theory, Methods, and Applications, is a volume undertaken by the friends and colleagues of Sid Yakowitz in his honor. Fifty internationally known scholars have collectively contributed 30 papers on modeling uncertainty to this volume. Each of these papers was carefully reviewed and in the majority of cases the original submission was revised before being accepted for publication in the book. The papers cover a great variety of topics in probability, statistics, economics, stochastic optimization, control theory, regression analysis, simulation, stochastic programming, Markov decision process, application in the HIV context, and others. There are papers with a theoretical emphasis and others that focus on applications. A number of papers survey the work in a particular area and in a few papers the authors present their personal view of a topic. It is a book with a considerable number of expository articles, which are accessible to a nonexpert - a graduate student in mathematics, statistics, engineering, and economics departments, or just anyone with some mathematical background who is interested in a preliminary exposition of a particular topic. Many of the papers present the state of the art of a specific area or represent original contributions which advance the present state of knowledge. In sum, it is a book of considerable interest to a broad range of academic researchers and students of stochastic systems.

Uncertainty Modeling

Author : Vladik Kreinovich
Publisher : Springer
ISBN 13 : 3319510525
Total Pages : 298 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis Uncertainty Modeling by : Vladik Kreinovich

Download or read book Uncertainty Modeling written by Vladik Kreinovich and published by Springer. This book was released on 2017-01-31 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book commemorates the 65th birthday of Dr. Boris Kovalerchuk, and reflects many of the research areas covered by his work. It focuses on data processing under uncertainty, especially fuzzy data processing, when uncertainty comes from the imprecision of expert opinions. The book includes 17 authoritative contributions by leading experts.

Modeling Uncertainty in the Earth Sciences

Author : Jef Caers
Publisher : John Wiley & Sons
ISBN 13 : 1119998719
Total Pages : 294 pages
Book Rating : 4.1/5 (199 download)

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Book Synopsis Modeling Uncertainty in the Earth Sciences by : Jef Caers

Download or read book Modeling Uncertainty in the Earth Sciences written by Jef Caers and published by John Wiley & Sons. This book was released on 2011-05-25 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling Uncertainty in the Earth Sciences highlights the various issues, techniques and practical modeling tools available for modeling the uncertainty of complex Earth systems and the impact that it has on practical situations. The aim of the book is to provide an introductory overview which covers a broad range of tried-and-tested tools. Descriptions of concepts, philosophies, challenges, methodologies and workflows give the reader an understanding of the best way to make decisions under uncertainty for Earth Science problems. The book covers key issues such as: Spatial and time aspect; large complexity and dimensionality; computation power; costs of 'engineering' the Earth; uncertainty in the modeling and decision process. Focusing on reliable and practical methods this book provides an invaluable primer for the complex area of decision making with uncertainty in the Earth Sciences.

Uncertainty

Author : William Briggs
Publisher : Springer
ISBN 13 : 3319397567
Total Pages : 274 pages
Book Rating : 4.3/5 (193 download)

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Book Synopsis Uncertainty by : William Briggs

Download or read book Uncertainty written by William Briggs and published by Springer. This book was released on 2016-07-15 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a philosophical approach to probability and probabilistic thinking, considering the underpinnings of probabilistic reasoning and modeling, which effectively underlie everything in data science. The ultimate goal is to call into question many standard tenets and lay the philosophical and probabilistic groundwork and infrastructure for statistical modeling. It is the first book devoted to the philosophy of data aimed at working scientists and calls for a new consideration in the practice of probability and statistics to eliminate what has been referred to as the "Cult of Statistical Significance." The book explains the philosophy of these ideas and not the mathematics, though there are a handful of mathematical examples. The topics are logically laid out, starting with basic philosophy as related to probability, statistics, and science, and stepping through the key probabilistic ideas and concepts, and ending with statistical models. Its jargon-free approach asserts that standard methods, such as out-of-the-box regression, cannot help in discovering cause. This new way of looking at uncertainty ties together disparate fields — probability, physics, biology, the “soft” sciences, computer science — because each aims at discovering cause (of effects). It broadens the understanding beyond frequentist and Bayesian methods to propose a Third Way of modeling.

Natural Hazard Uncertainty Assessment

Author : Karin Riley
Publisher : John Wiley & Sons
ISBN 13 : 1119027861
Total Pages : 356 pages
Book Rating : 4.1/5 (19 download)

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Book Synopsis Natural Hazard Uncertainty Assessment by : Karin Riley

Download or read book Natural Hazard Uncertainty Assessment written by Karin Riley and published by John Wiley & Sons. This book was released on 2016-12-12 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertainties are pervasive in natural hazards, and it is crucial to develop robust and meaningful approaches to characterize and communicate uncertainties to inform modeling efforts. In this monograph we provide a broad, cross-disciplinary overview of issues relating to uncertainties faced in natural hazard and risk assessment. We introduce some basic tenets of uncertainty analysis, discuss issues related to communication and decision support, and offer numerous examples of analyses and modeling approaches that vary by context and scope. Contributors include scientists from across the full breath of the natural hazard scientific community, from those in real-time analysis of natural hazards to those in the research community from academia and government. Key themes and highlights include: Substantial breadth and depth of analysis in terms of the types of natural hazards addressed, the disciplinary perspectives represented, and the number of studies included Targeted, application-centered analyses with a focus on development and use of modeling techniques to address various sources of uncertainty Emphasis on the impacts of climate change on natural hazard processes and outcomes Recommendations for cross-disciplinary and science transfer across natural hazard sciences This volume will be an excellent resource for those interested in the current work on uncertainty classification/quantification and will document common and emergent research themes to allow all to learn from each other and build a more connected but still diverse and ever growing community of scientists. Read an interview with the editors to find out more: https://eos.org/editors-vox/reducing-uncertainty-in-hazard-prediction

The Uncertainty Analysis of Model Results

Author : Eduard Hofer
Publisher : Springer
ISBN 13 : 9783319762968
Total Pages : 346 pages
Book Rating : 4.7/5 (629 download)

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Book Synopsis The Uncertainty Analysis of Model Results by : Eduard Hofer

Download or read book The Uncertainty Analysis of Model Results written by Eduard Hofer and published by Springer. This book was released on 2018-07-01 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a practical guide to the uncertainty analysis of computer model applications. Used in many areas, such as engineering, ecology and economics, computer models are subject to various uncertainties at the level of model formulations, parameter values and input data. Naturally, it would be advantageous to know the combined effect of these uncertainties on the model results as well as whether the state of knowledge should be improved in order to reduce the uncertainty of the results most effectively. The book supports decision-makers, model developers and users in their argumentation for an uncertainty analysis and assists them in the interpretation of the analysis results.

Probability and Bayesian Modeling

Author : Jim Albert
Publisher : CRC Press
ISBN 13 : 1351030132
Total Pages : 553 pages
Book Rating : 4.3/5 (51 download)

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Book Synopsis Probability and Bayesian Modeling by : Jim Albert

Download or read book Probability and Bayesian Modeling written by Jim Albert and published by CRC Press. This book was released on 2019-12-06 with total page 553 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probability and Bayesian Modeling is an introduction to probability and Bayesian thinking for undergraduate students with a calculus background. The first part of the book provides a broad view of probability including foundations, conditional probability, discrete and continuous distributions, and joint distributions. Statistical inference is presented completely from a Bayesian perspective. The text introduces inference and prediction for a single proportion and a single mean from Normal sampling. After fundamentals of Markov Chain Monte Carlo algorithms are introduced, Bayesian inference is described for hierarchical and regression models including logistic regression. The book presents several case studies motivated by some historical Bayesian studies and the authors’ research. This text reflects modern Bayesian statistical practice. Simulation is introduced in all the probability chapters and extensively used in the Bayesian material to simulate from the posterior and predictive distributions. One chapter describes the basic tenets of Metropolis and Gibbs sampling algorithms; however several chapters introduce the fundamentals of Bayesian inference for conjugate priors to deepen understanding. Strategies for constructing prior distributions are described in situations when one has substantial prior information and for cases where one has weak prior knowledge. One chapter introduces hierarchical Bayesian modeling as a practical way of combining data from different groups. There is an extensive discussion of Bayesian regression models including the construction of informative priors, inference about functions of the parameters of interest, prediction, and model selection. The text uses JAGS (Just Another Gibbs Sampler) as a general-purpose computational method for simulating from posterior distributions for a variety of Bayesian models. An R package ProbBayes is available containing all of the book datasets and special functions for illustrating concepts from the book. A complete solutions manual is available for instructors who adopt the book in the Additional Resources section.

Uncertainty Theory

Author : Baoding Liu
Publisher : Springer
ISBN 13 : 3540731652
Total Pages : 263 pages
Book Rating : 4.5/5 (47 download)

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Book Synopsis Uncertainty Theory by : Baoding Liu

Download or read book Uncertainty Theory written by Baoding Liu and published by Springer. This book was released on 2007-09-14 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a self-contained, comprehensive and up-to-date presentation of uncertainty theory. The purpose is to equip the readers with an axiomatic approach to deal with uncertainty. For this new edition the entire text has been totally rewritten. The chapters on chance theory and uncertainty theory are completely new. Mathematicians, researchers, engineers, designers, and students will find this work a stimulating and useful reference.

Statistical Thinking: a Simulation Approach to Modeling Uncertainty

Author : Andrew Zieffler
Publisher :
ISBN 13 : 9780615691305
Total Pages : 176 pages
Book Rating : 4.6/5 (913 download)

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Book Synopsis Statistical Thinking: a Simulation Approach to Modeling Uncertainty by : Andrew Zieffler

Download or read book Statistical Thinking: a Simulation Approach to Modeling Uncertainty written by Andrew Zieffler and published by . This book was released on 2012-08-29 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning statistics is sexy.Almost every person on earth will benefit from learning some foundational ideas of statistics. This is true because statistics forms the basis of our everyday world just as much as do science, technology, and politics. Google, Netflix, Twitter, Facebook, OKCupid, Match.com, Amazon, iTunes, and the Federal Government are just a handful of the companies and organizations that use statistics on a daily basis. Journalism, political science, biology, sociology, psychology, graphic design, economics, sports science, and dance are all disciplines that have made use of statistical methodology.The materials in this book will introduce you to the seminal ideas underlying the discipline of statistics. In addition, they have been designed with your learning in mind. As you engage in and use the skills, concepts and ideas introduced in the material, you will find yourself thinking about data and evidence in a different way.

Uncertainty Modelling in Data Science

Author : Sébastien Destercke
Publisher : Springer
ISBN 13 : 3319975471
Total Pages : 246 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Uncertainty Modelling in Data Science by : Sébastien Destercke

Download or read book Uncertainty Modelling in Data Science written by Sébastien Destercke and published by Springer. This book was released on 2018-07-24 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features 29 peer-reviewed papers presented at the 9th International Conference on Soft Methods in Probability and Statistics (SMPS 2018), which was held in conjunction with the 5th International Conference on Belief Functions (BELIEF 2018) in Compiègne, France on September 17–21, 2018. It includes foundational, methodological and applied contributions on topics as varied as imprecise data handling, linguistic summaries, model coherence, imprecise Markov chains, and robust optimisation. These proceedings were produced using EasyChair. Over recent decades, interest in extensions and alternatives to probability and statistics has increased significantly in diverse areas, including decision-making, data mining and machine learning, and optimisation. This interest stems from the need to enrich existing models, in order to include different facets of uncertainty, like ignorance, vagueness, randomness, conflict or imprecision. Frameworks such as rough sets, fuzzy sets, fuzzy random variables, random sets, belief functions, possibility theory, imprecise probabilities, lower previsions, and desirable gambles all share this goal, but have emerged from different needs. The advances, results and tools presented in this book are important in the ubiquitous and fast-growing fields of data science, machine learning and artificial intelligence. Indeed, an important aspect of some of the learned predictive models is the trust placed in them. Modelling the uncertainty associated with the data and the models carefully and with principled methods is one of the means of increasing this trust, as the model will then be able to distinguish between reliable and less reliable predictions. In addition, extensions such as fuzzy sets can be explicitly designed to provide interpretable predictive models, facilitating user interaction and increasing trust.

Uncertainty Quantification in Multiscale Materials Modeling

Author : Yan Wang
Publisher : Woodhead Publishing
ISBN 13 : 0081029411
Total Pages : 604 pages
Book Rating : 4.0/5 (81 download)

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Book Synopsis Uncertainty Quantification in Multiscale Materials Modeling by : Yan Wang

Download or read book Uncertainty Quantification in Multiscale Materials Modeling written by Yan Wang and published by Woodhead Publishing. This book was released on 2020-03-12 with total page 604 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertainty Quantification in Multiscale Materials Modeling provides a complete overview of uncertainty quantification (UQ) in computational materials science. It provides practical tools and methods along with examples of their application to problems in materials modeling. UQ methods are applied to various multiscale models ranging from the nanoscale to macroscale. This book presents a thorough synthesis of the state-of-the-art in UQ methods for materials modeling, including Bayesian inference, surrogate modeling, random fields, interval analysis, and sensitivity analysis, providing insight into the unique characteristics of models framed at each scale, as well as common issues in modeling across scales.

Uncertainty Analysis with High Dimensional Dependence Modelling

Author : Dorota Kurowicka
Publisher : John Wiley & Sons
ISBN 13 : 0470863080
Total Pages : 302 pages
Book Rating : 4.4/5 (78 download)

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Book Synopsis Uncertainty Analysis with High Dimensional Dependence Modelling by : Dorota Kurowicka

Download or read book Uncertainty Analysis with High Dimensional Dependence Modelling written by Dorota Kurowicka and published by John Wiley & Sons. This book was released on 2006-10-02 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical models are used to simulate complex real-world phenomena in many areas of science and technology. Large complex models typically require inputs whose values are not known with certainty. Uncertainty analysis aims to quantify the overall uncertainty within a model, in order to support problem owners in model-based decision-making. In recent years there has been an explosion of interest in uncertainty analysis. Uncertainty and dependence elicitation, dependence modelling, model inference, efficient sampling, screening and sensitivity analysis, and probabilistic inversion are among the active research areas. This text provides both the mathematical foundations and practical applications in this rapidly expanding area, including: An up-to-date, comprehensive overview of the foundations and applications of uncertainty analysis. All the key topics, including uncertainty elicitation, dependence modelling, sensitivity analysis and probabilistic inversion. Numerous worked examples and applications. Workbook problems, enabling use for teaching. Software support for the examples, using UNICORN - a Windows-based uncertainty modelling package developed by the authors. A website featuring a version of the UNICORN software tailored specifically for the book, as well as computer programs and data sets to support the examples. Uncertainty Analysis with High Dimensional Dependence Modelling offers a comprehensive exploration of a new emerging field. It will prove an invaluable text for researches, practitioners and graduate students in areas ranging from statistics and engineering to reliability and environmetrics.

Inferential Models

Author : Ryan Martin
Publisher : CRC Press
ISBN 13 : 1439886512
Total Pages : 274 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Inferential Models by : Ryan Martin

Download or read book Inferential Models written by Ryan Martin and published by CRC Press. This book was released on 2015-09-25 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: A New Approach to Sound Statistical ReasoningInferential Models: Reasoning with Uncertainty introduces the authors' recently developed approach to inference: the inferential model (IM) framework. This logical framework for exact probabilistic inference does not require the user to input prior information. The authors show how an IM produces meaning

Uncertainty Quantification and Stochastic Modeling with Matlab

Author : Eduardo Souza de Cursi
Publisher : Elsevier
ISBN 13 : 0081004710
Total Pages : 457 pages
Book Rating : 4.0/5 (81 download)

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Book Synopsis Uncertainty Quantification and Stochastic Modeling with Matlab by : Eduardo Souza de Cursi

Download or read book Uncertainty Quantification and Stochastic Modeling with Matlab written by Eduardo Souza de Cursi and published by Elsevier. This book was released on 2015-04-09 with total page 457 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertainty Quantification (UQ) is a relatively new research area which describes the methods and approaches used to supply quantitative descriptions of the effects of uncertainty, variability and errors in simulation problems and models. It is rapidly becoming a field of increasing importance, with many real-world applications within statistics, mathematics, probability and engineering, but also within the natural sciences. Literature on the topic has up until now been largely based on polynomial chaos, which raises difficulties when considering different types of approximation and does not lead to a unified presentation of the methods. Moreover, this description does not consider either deterministic problems or infinite dimensional ones. This book gives a unified, practical and comprehensive presentation of the main techniques used for the characterization of the effect of uncertainty on numerical models and on their exploitation in numerical problems. In particular, applications to linear and nonlinear systems of equations, differential equations, optimization and reliability are presented. Applications of stochastic methods to deal with deterministic numerical problems are also discussed. Matlab® illustrates the implementation of these methods and makes the book suitable as a textbook and for self-study. - Discusses the main ideas of Stochastic Modeling and Uncertainty Quantification using Functional Analysis - Details listings of Matlab® programs implementing the main methods which complete the methodological presentation by a practical implementation - Construct your own implementations from provided worked examples