Degrees Of Uncertainty
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Degree Of Uncertainty
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Author : NICOLA. SMITH
language : en
Publisher:
Release Date : 2019
Degree Of Uncertainty written by NICOLA. SMITH and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Brain Correlates Of Uncertain Decisions
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Author : Kirsten G. Volz
language : en
Publisher:
Release Date : 2004
Brain Correlates Of Uncertain Decisions written by Kirsten G. Volz and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Brain categories.
Uncertainty Theory
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Author : Baoding Liu
language : en
Publisher: Springer
Release Date : 2014-11-03
Uncertainty Theory written by Baoding Liu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-03 with Technology & Engineering categories.
When no samples are available to estimate a probability distribution, we have to invite some domain experts to evaluate the belief degree that each event will happen. Perhaps some people think that the belief degree should be modeled by subjective probability or fuzzy set theory. However, it is usually inappropriate because both of them may lead to counterintuitive results in this case. In order to rationally deal with belief degrees, uncertainty theory was founded in 2007 and subsequently studied by many researchers. Nowadays, uncertainty theory has become a branch of axiomatic mathematics for modeling belief degrees. This is an introductory textbook on uncertainty theory, uncertain programming, uncertain statistics, uncertain risk analysis, uncertain reliability analysis, uncertain set, uncertain logic, uncertain inference, uncertain process, uncertain calculus, and uncertain differential equation. This textbook also shows applications of uncertainty theory to scheduling, logistics, networks, data mining, control, and finance.
Symbolic And Quantitative Approaches To Reasoning With Uncertainty
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Author : Salem Benferhat
language : en
Publisher: Springer
Release Date : 2003-06-30
Symbolic And Quantitative Approaches To Reasoning With Uncertainty written by Salem Benferhat and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-06-30 with Computers categories.
This book constitutes the refereed proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2001, held in Toulouse, France in September 2001. The 68 revised full papers presented together with three invited papers were carefully reviewed and selected from over a hundred submissions. The book offers topical sections on decision theory, partially observable Markov decision processes, decision-making, coherent probabilities, Bayesian networks, learning causal networks, graphical representation of uncertainty, imprecise probabilities, belief functions, fuzzy sets and rough sets, possibility theory, merging, belief revision and preferences, inconsistency handling, default logic, logic programming, etc.
Degrees Of Uncertainty
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Author : Erika Renee Shaker
language : en
Publisher:
Release Date : 2013
Degrees Of Uncertainty written by Erika Renee Shaker and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with College students categories.
Introduction -- Muted provincial response -- Deal or no deal? -- System progressivity -- International comparisons -- Provincial overviews -- Conclusions -- Notes.
Decision Making Under Uncertainty
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Author : Kerstin Preuschoff
language : en
Publisher: Frontiers Media SA
Release Date : 2015-06-16
Decision Making Under Uncertainty written by Kerstin Preuschoff and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-16 with Biological psychiatry categories.
Most decisions in life are based on incomplete information and have uncertain consequences. To successfully cope with real-life situations, the nervous system has to estimate, represent and eventually resolve uncertainty at various levels. A common tradeoff in such decisions involves those between the magnitude of the expected rewards and the uncertainty of obtaining the rewards. For instance, a decision maker may choose to forgo the high expected rewards of investing in the stock market and settle instead for the lower expected reward and much less uncertainty of a savings account. Little is known about how different forms of uncertainty, such as risk or ambiguity, are processed and learned about and how they are integrated with expected rewards and individual preferences throughout the decision making process. With this Research Topic we aim to provide a deeper and more detailed understanding of the processes behind decision making under uncertainty.
Three Domain Modelling And Uncertainty Analysis
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Author : Atom Mirakyan
language : en
Publisher: Springer
Release Date : 2015-05-28
Three Domain Modelling And Uncertainty Analysis written by Atom Mirakyan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-05-28 with Business & Economics categories.
This book examines in detail the planning and modelling of local infrastructure like energy systems, including the complexities resulting from various uncertainties. Readers will discover the individual steps involved in infrastructure planning in cities and territories, as well as the primary requirements and supporting quality factors. Further topics covered concern the field of uncertainty and its synergies with infrastructure planning. Theories, methodological backgrounds and concrete case studies will not only help readers to understand the proposed methodologies for modelling and uncertainty analysis, but will also show them how these approaches are implemented in practice.
Measurement Uncertainty
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Author : Ronald H. Dieck
language : en
Publisher: ISA
Release Date : 2007
Measurement Uncertainty written by Ronald H. Dieck and has been published by ISA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Mathematics categories.
Literally an entire course between two covers, Measurement Uncertainty: Methods and Applications, Fourth Edition, presents engineering students with a comprehensive tutorial of measurement uncertainty methods in a logically categorized and readily utilized format. The new uncertainty technologies embodied in both U.S. and international standards have been incorporated into this text with a view toward understanding the strengths and weaknesses of both. The book is designed to also serve as a practical desk reference in situations that commonly confront an experimenter. The text presents the basics of the measurement uncertainty model, non-symmetrical systematic standard uncertainties, random standard uncertainties, the use of correlation, curve-fitting problems, and probability plotting, combining results from different test methods, calibration errors, and uncertainty propagation for both independent and dependent error sources. The author draws on years of experience in industry to direct special attention to the problem of developing confidence in uncertainty analysis results and using measurement uncertainty to select instrumentation systems.
J M Keynes On The Definition Of Uncertainty
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Author : Michael Emmett Brady
language : en
Publisher:
Release Date : 2016
J M Keynes On The Definition Of Uncertainty written by Michael Emmett Brady and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
Keynes's definition of uncertainty is directly based on his weight of the argument (evidence) relation, analyzed in chapters 6 and 26 of the A Treatise on Probability (1921), page 148, as well as the footnote on page 148, of the General Theory (1936), and multiple pages of his February, 1937 Quarterly Journal of Economics article. There is no discussion of the definition of Uncertainty in his exchanges with Jan Tinbergen in 1939-40 in the Economic Journal. Paul Davidson and his Post Keynesian-Institutionalist supporters base their Ergodic-Non Ergodic approach to the definitions of uncertainty and risk on the inductive fallacy of Conditional A priorism (Long Runism). The claim, made by Paul Davidson and his Post Keynesian-Institutionalist supporters for over 30 years, that decision makers are able to identify the ergodicity or non ergodicity of long run stochastic sequences or series of events or outcomes in the short run, based on Davidson's claim that decision makers are able to know or learn of the convergence properties of such series or sequences, which can only be known "in the long run" (infinity), by examining sub series or sub sequences, is patently false and not accepted by any scholar in any other academic field. Davidson bases his binary approach to uncertainty, which rules out any concept of different degrees to knowledge (certainty) and unknowledge (uncertainty), on both metaphysical speculations and/or a priori claims to knowledge. There can be no such thing as probable knowledge under this binary approach.
Uncertainty In Artificial Intelligence
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Author : David Heckerman
language : en
Publisher: Morgan Kaufmann
Release Date : 2014-05-12
Uncertainty In Artificial Intelligence written by David Heckerman and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-12 with Computers categories.
Uncertainty in Artificial Intelligence contains the proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence held at the Catholic University of America in Washington, DC, on July 9-11, 1993. The papers focus on methods of reasoning and decision making under uncertainty as applied to problems in artificial intelligence (AI) and cover topics ranging from knowledge acquisition and automated model construction to learning, planning, temporal reasoning, and machine vision. Comprised of 66 chapters, this book begins with a discussion on causality in Bayesian belief networks before turning to a decision theoretic account of conditional ought statements that rectifies glaring deficiencies in classical deontic logic and forms a sound basis for qualitative decision theory. Subsequent chapters explore trade-offs in constructing and evaluating temporal influence diagrams; normative engineering risk management systems; additive belief-network models; and sensitivity analysis for probability assessments in Bayesian networks. Automated model construction and learning as well as algorithms for inference and decision making are also considered. This monograph will be of interest to both students and practitioners in the fields of AI and computer science.