Handbook of Uncertainty Quantification - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Handbook of Uncertainty Quantification write by Roger Ghanem. This book was released on 2017. Handbook of Uncertainty Quantification available in PDF, EPUB and Kindle.
Handbook of Uncertainty Quantification
Handbook of Uncertainty Quantification - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Handbook of Uncertainty Quantification write by Roger Ghanem. This book was released on 2016-05-08. Handbook of Uncertainty Quantification available in PDF, EPUB and Kindle. The topic of Uncertainty Quantification (UQ) has witnessed massive developments in response to the promise of achieving risk mitigation through scientific prediction. It has led to the integration of ideas from mathematics, statistics and engineering being used to lend credence to predictive assessments of risk but also to design actions (by engineers, scientists and investors) that are consistent with risk aversion. The objective of this Handbook is to facilitate the dissemination of the forefront of UQ ideas to their audiences. We recognize that these audiences are varied, with interests ranging from theory to application, and from research to development and even execution.
Uncertainty Quantification
Uncertainty Quantification - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Uncertainty Quantification write by Ralph C. Smith. This book was released on 2013-12-02. Uncertainty Quantification available in PDF, EPUB and Kindle. The field of uncertainty quantification is evolving rapidly because of increasing emphasis on models that require quantified uncertainties for large-scale applications, novel algorithm development, and new computational architectures that facilitate implementation of these algorithms. Uncertainty Quantification: Theory, Implementation, and Applications provides readers with the basic concepts, theory, and algorithms necessary to quantify input and response uncertainties for simulation models arising in a broad range of disciplines. The book begins with a detailed discussion of applications where uncertainty quantification is critical for both scientific understanding and policy. It then covers concepts from probability and statistics, parameter selection techniques, frequentist and Bayesian model calibration, propagation of uncertainties, quantification of model discrepancy, surrogate model construction, and local and global sensitivity analysis. The author maintains a complementary web page where readers can find data used in the exercises and other supplementary material.
Introduction to Uncertainty Quantification
Introduction to Uncertainty Quantification - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Introduction to Uncertainty Quantification write by T.J. Sullivan. This book was released on 2015-12-14. Introduction to Uncertainty Quantification available in PDF, EPUB and Kindle. This text provides a framework in which the main objectives of the field of uncertainty quantification (UQ) are defined and an overview of the range of mathematical methods by which they can be achieved. Complete with exercises throughout, the book will equip readers with both theoretical understanding and practical experience of the key mathematical and algorithmic tools underlying the treatment of uncertainty in modern applied mathematics. Students and readers alike are encouraged to apply the mathematical methods discussed in this book to their own favorite problems to understand their strengths and weaknesses, also making the text suitable for a self-study. Uncertainty quantification is a topic of increasing practical importance at the intersection of applied mathematics, statistics, computation and numerous application areas in science and engineering. This text is designed as an introduction to UQ for senior undergraduate and graduate students with a mathematical or statistical background and also for researchers from the mathematical sciences or from applications areas who are interested in the field. T. J. Sullivan was Warwick Zeeman Lecturer at the Mathematics Institute of the University of Warwick, United Kingdom, from 2012 to 2015. Since 2015, he is Junior Professor of Applied Mathematics at the Free University of Berlin, Germany, with specialism in Uncertainty and Risk Quantification.
Handbook of Probabilistic Models
Handbook of Probabilistic Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Handbook of Probabilistic Models write by Pijush Samui. This book was released on 2019-10-05. Handbook of Probabilistic Models available in PDF, EPUB and Kindle. Handbook of Probabilistic Models carefully examines the application of advanced probabilistic models in conventional engineering fields. In this comprehensive handbook, practitioners, researchers and scientists will find detailed explanations of technical concepts, applications of the proposed methods, and the respective scientific approaches needed to solve the problem. This book provides an interdisciplinary approach that creates advanced probabilistic models for engineering fields, ranging from conventional fields of mechanical engineering and civil engineering, to electronics, electrical, earth sciences, climate, agriculture, water resource, mathematical sciences and computer sciences. Specific topics covered include minimax probability machine regression, stochastic finite element method, relevance vector machine, logistic regression, Monte Carlo simulations, random matrix, Gaussian process regression, Kalman filter, stochastic optimization, maximum likelihood, Bayesian inference, Bayesian update, kriging, copula-statistical models, and more. Explains the application of advanced probabilistic models encompassing multidisciplinary research Applies probabilistic modeling to emerging areas in engineering Provides an interdisciplinary approach to probabilistic models and their applications, thus solving a wide range of practical problems
