Download or read book Risk and Uncertainty Reduction by Using Algebraic Inequalities written by Michael T. Todinov and published by CRC Press. This book was released on 2020-06-02 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the application of algebraic inequalities for reliability improvement and for uncertainty and risk reduction. It equips readers with powerful domain-independent methods for reducing risk based on algebraic inequalities and demonstrates the significant benefits derived from the application for risk and uncertainty reduction. Algebraic inequalities: • Provide a powerful reliability improvement, risk and uncertainty reduction method that transcends engineering and can be applied in various domains of human activity • Present an effective tool for dealing with deep uncertainty related to key reliability-critical parameters of systems and processes • Permit meaningful interpretations which link abstract inequalities with the real world • Offer a tool for determining tight bounds for the variation of risk-critical parameters and complying the design with these bounds to avoid failure • Allow optimising designs and processes by minimising the deviation of critical output parameters from their specified values and maximising their performance This book is primarily for engineering professionals and academic researchers in virtually all existing engineering disciplines.
Download or read book Risk and Uncertainty Reduction by Using Algebraic Inequalities written by M. T. Todinov and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book provides the reader with a domain-independent method for reducing risk through maximizing reliability, reducing epistemic uncertainty, reducing aleatory uncertainty, ranking the reliabilities of systems and processes, minimizing the risk of faulty assemblies, and ranking decision-making alternatives in the presence of deep uncertainty"--
Download or read book Interpretation of Algebraic Inequalities written by Michael Todinov and published by CRC Press. This book was released on 2021-10-13 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces a new method based on algebraic inequalities for optimising engineering systems and processes, with applications in mechanical engineering, materials science, electrical engineering, reliability engineering, risk management and operational research. This book shows that the application potential of algebraic inequalities in engineering and technology is far-reaching and certainly not restricted to specifying design constraints. Algebraic inequalities can handle deep uncertainty associated with design variables and control parameters. With the method presented in this book, powerful new knowledge about systems and processes can be generated through meaningful interpretation of algebraic inequalities. This book demonstrates how the generated knowledge can be put into practice through covering the algebraic inequalities suitable for interpretation in different contexts and describing how to apply this knowledge to enhance system and process performance. Depending on the specific interpretation, knowledge, applicable to different systems from different application domains, can be generated from the same algebraic inequality. Furthermore, an important class of algebraic inequalities has been introduced that can be used for optimising systems and processes in any area of science and technology provided that the variables and the separate terms of the inequalities are additive quantities. With the presented various examples and solutions, this book will be of interest to engineers, students and researchers in the field of optimisation, engineering design, reliability engineering, risk management and operational research.
Download or read book Handbook of Advanced Performability Engineering written by Krishna B. Misra and published by Springer Nature. This book was released on 2020-11-16 with total page 811 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book considers all aspects of performability engineering, providing a holistic view of the activities associated with a product throughout its entire life cycle of the product, as well as the cost of minimizing the environmental impact at each stage, while maximizing the performance. Building on the editor's previous Handbook of Performability Engineering, it explains how performability engineering provides us with a framework to consider both dependability and sustainability in the optimal design of products, systems and services, and explores the role of performability in energy and waste minimization, raw material selection, increased production volume, and many other areas of engineering and production. The book discusses a range of new ideas, concepts, disciplines, and applications in performability, including smart manufacturing and Industry 4.0; cyber-physical systems and artificial intelligence; digital transformation of railways; and asset management. Given its broad scope, it will appeal to researchers, academics, industrial practitioners and postgraduate students involved in manufacturing, engineering, and system and product development.
Download or read book Interpretation of Algebraic Inequalities written by M. T. Todinov and published by . This book was released on 2021-10-13 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces a new method based on algebraic inequalities for optimising engineering systems and processes, with applications in mechanical engineering, materials science, electrical engineering, reliability engineering, risk management and operational research. This book shows that the application potential of algebraic inequalities in engineering and technology is far-reaching and certainly not restricted to specifying design constraints. Algebraic inequalities can handle deep uncertainty associated with design variables and control parameters. With the method presented in this book, powerful new knowledge about systems and processes can be generated through meaningful interpretation of algebraic inequalities. This book demonstrates how the generated knowledge can be put into practice through covering the algebraic inequalities suitable for interpretation in different contexts and describing how to apply this knowledge to enhance system and process performance. Depending on the specific interpretation, knowledge, applicable to different systems from different application domains, can be generated from the same algebraic inequality. Furthermore, an important class of algebraic inequalities has been introduced that can be used for optimising systems and processes in any area of science and technology provided that the variables and the separate terms of the inequalities are additive quantities. With the presented various examples and solutions, this book will be of interest to engineers, students and researchers in the field of optimisation, engineering design, reliability engineering, risk management and operational research.
Download or read book Uncertainty Quantification in Variational Inequalities written by Joachim Gwinner and published by CRC Press. This book was released on 2021-12-21 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertainty Quantification (UQ) is an emerging and extremely active research discipline which aims to quantitatively treat any uncertainty in applied models. The primary objective of Uncertainty Quantification in Variational Inequalities: Theory, Numerics, and Applications is to present a comprehensive treatment of UQ in variational inequalities and some of its generalizations emerging from various network, economic, and engineering models. Some of the developed techniques also apply to machine learning, neural networks, and related fields. Features First book on UQ in variational inequalities emerging from various network, economic, and engineering models Completely self-contained and lucid in style Aimed for a diverse audience including applied mathematicians, engineers, economists, and professionals from academia Includes the most recent developments on the subject which so far have only been available in the research literature
Download or read book Oracle Inequalities in Empirical Risk Minimization and Sparse Recovery Problems written by Vladimir Koltchinskii and published by Springer. This book was released on 2011-07-29 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of these lecture notes is to provide an introduction to the general theory of empirical risk minimization with an emphasis on excess risk bounds and oracle inequalities in penalized problems. In recent years, there have been new developments in this area motivated by the study of new classes of methods in machine learning such as large margin classification methods (boosting, kernel machines). The main probabilistic tools involved in the analysis of these problems are concentration and deviation inequalities by Talagrand along with other methods of empirical processes theory (symmetrization inequalities, contraction inequality for Rademacher sums, entropy and generic chaining bounds). Sparse recovery based on l_1-type penalization and low rank matrix recovery based on the nuclear norm penalization are other active areas of research, where the main problems can be stated in the framework of penalized empirical risk minimization, and concentration inequalities and empirical processes tools have proved to be very useful.
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 255 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.
Download or read book Methods for Reliability Improvement and Risk Reduction written by Michael Todinov and published by John Wiley & Sons. This book was released on 2018-12-10 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reliability is one of the most important attributes for the products and processes of any company or organization. This important work provides a powerful framework of domain-independent reliability improvement and risk reducing methods which can greatly lower risk in any area of human activity. It reviews existing methods for risk reduction that can be classified as domain-independent and introduces the following new domain-independent reliability improvement and risk reduction methods: Separation Stochastic separation Introducing deliberate weaknesses Segmentation Self-reinforcement Inversion Reducing the rate of accumulation of damage Permutation Substitution Limiting the space and time exposure Comparative reliability models The domain-independent methods for reliability improvement and risk reduction do not depend on the availability of past failure data, domain-specific expertise or knowledge of the failure mechanisms underlying the failure modes. Through numerous examples and case studies, this invaluable guide shows that many of the new domain-independent methods improve reliability at no extra cost or at a low cost. Using the proven methods in this book, any company and organisation can greatly enhance the reliability of its products and operations.
Download or read book Operations Management Analysis for Decisions written by Gene K. Groff and published by . This book was released on 1972 with total page 590 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Decision Making Under Uncertainty written by Mykel J. Kochenderfer and published by MIT Press. This book was released on 2015-07-24 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.
Download or read book Measuring Social Welfare written by Matthew D. Adler and published by Oxford University Press, USA. This book was released on 2019 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: Disputes over government policies rage in a number of areas. From taxation to climate change, from public finance to risk regulation, and from health care to infrastructure planning, advocates debate how policies affect multiple dimensions of individual well-being, how these effects balance against each other, and how trade-offs between overall well-being and inequality should be resolved. How to measure and balance well-being gains and losses is a vexed issue. Matthew D. Adler advances the debate by introducing the social welfare function (SWF) framework and demonstrating how it can be used as a powerful tool for evaluating governmental policies. The framework originates in welfare economics and in philosophical scholarship regarding individual well-being, ethics, and distributive justice. It has three core components: a well-being measure, which translates each of the possible policy outcomes into an array of interpersonally comparable well-being numbers, quantifying how well off each person in the population would be in that outcome; a rule for ranking outcomes thus described; and an uncertainty module, which orders policies understood as probability distributions over outcomes. The SWF framework is a significant improvement compared to cost-benefit analysis (CBA), which quantifies policy impacts in dollars, is thereby biased towards the rich, and is insensitive to the distribution of these monetized impacts. The SWF framework, by contrast, uses an unbiased measure of well-being and allows the policymaker to consider both efficiency (total well-being) and equity (the distribution of well-being). Because the SWF framework is a fully generic methodology for policy assessment, Adler also discusses how it can be implemented to inform government policies. He illustrates it through a detailed case study of risk regulation, contrasting the implications of results of SWF and CBA. This book provides an accessible, yet rigorous overview of the SWF approach that can inform policy-makers and students.
Download or read book Robust Optimization written by Aharon Ben-Tal and published by Princeton University Press. This book was released on 2009-08-10 with total page 576 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robust optimization is still a relatively new approach to optimization problems affected by uncertainty, but it has already proved so useful in real applications that it is difficult to tackle such problems today without considering this powerful methodology. Written by the principal developers of robust optimization, and describing the main achievements of a decade of research, this is the first book to provide a comprehensive and up-to-date account of the subject. Robust optimization is designed to meet some major challenges associated with uncertainty-affected optimization problems: to operate under lack of full information on the nature of uncertainty; to model the problem in a form that can be solved efficiently; and to provide guarantees about the performance of the solution. The book starts with a relatively simple treatment of uncertain linear programming, proceeding with a deep analysis of the interconnections between the construction of appropriate uncertainty sets and the classical chance constraints (probabilistic) approach. It then develops the robust optimization theory for uncertain conic quadratic and semidefinite optimization problems and dynamic (multistage) problems. The theory is supported by numerous examples and computational illustrations. An essential book for anyone working on optimization and decision making under uncertainty, Robust Optimization also makes an ideal graduate textbook on the subject.
Download or read book The Economics of Risk and Time written by Christian Gollier and published by MIT Press. This book was released on 2001 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt: Updates and advances the theory of expected utility as applied to risk analysis and financial decision making.
Download or read book INFORMS Conference Program written by Institute for Operations Research and the Management Sciences. National Meeting and published by . This book was released on 1996 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book An Introduction to Matrix Concentration Inequalities written by Joel Tropp and published by . This book was released on 2015-05-27 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Random matrices now play a role in many areas of theoretical, applied, and computational mathematics. It is therefore desirable to have tools for studying random matrices that are flexible, easy to use, and powerful. Over the last fifteen years, researchers have developed a remarkable family of results, called matrix concentration inequalities, that achieve all of these goals. This monograph offers an invitation to the field of matrix concentration inequalities. It begins with some history of random matrix theory; it describes a flexible model for random matrices that is suitable for many problems; and it discusses the most important matrix concentration results. To demonstrate the value of these techniques, the presentation includes examples drawn from statistics, machine learning, optimization, combinatorics, algorithms, scientific computing, and beyond.
Download or read book Mathematics for Machine Learning written by Marc Peter Deisenroth and published by Cambridge University Press. This book was released on 2020-04-23 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
