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 Kreinovich, Vladik.
 Cham : Springer, 2023.
 Description
 Book — 1 online resource (125 p.).
 Summary

 Motivation and Outline. A General Description of Measuring Devices: Plan. A General Description of Measuring Devices: First Step  Finite Set of Possible Outcomes. A General Description of Measuring Devices: Second Step  Pairs of Compatible Outcomes. A General Description of Measuring Devices: Third Step  Subsets of Compatible Outcomes. A General Description of Measuring Devices: Fourth Step  Conditional Statements about Possible Outcomes. A General Description of Measuring Devices: Fifth Step  Disjunctive Conditional Statements about the Possible Outcomes. A General Description of Measuring Devices: Summary. Physical Quantities: A General Description. Properties of Physical Quantities. Future Work.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Kreinovich, Vladik.
 Cham, Switzerland : Springer, 2022.
 Description
 Book — 1 online resource
 Summary

 Why Explainable AI? Why Fuzzy Explainable AI? What Is Fuzzy?. Defuzzification. Which Fuzzy Techniques?. So How Can We Design Explainable Fuzzy AI: Ideas. How to Make Machine Learning Itself More Explainable. Final SelfTest.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Kreinovich, Vladik.
 Moskva : [s.n.], 1997.
 Description
 Book — 110 p. ; 14 cm.
 Online
Green Library
Green Library  Status 

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PG3482.8 .R39 M65 1997 T  Unknown 
 Kreinovich, Vladik.
 [Washington, D.C. : National Aeronautics and Space Administration ; Springfield, Va. : National Technical Information Service, distributor, 1996]
 Description
 Book — 1 v.
 Online
Green Library
Green Library  Status 

Find it US Federal Documents  
NAS 1.26:202660  Unknown 
 Kreinovich, Vladik.
 [Washington, DC? : National Aeronautics and Space Administration ; Springfield, Va. : National Technical Information Service, distributor, 1993]
 Description
 Book — 1 v.
 Online
Green Library
Green Library  Status 

Find it US Federal Documents  
NAS 1.26:192950  Unknown 
 Kreinovich, Vladik.
 El Paso, TX : University of Texas at El Paso ; [Washington, DC : National Aeronautics and Space Administration, 1993]
 Description
 Book — 1 v.
 Online
Green Library
Green Library  Status 

Find it US Federal Documents  
NAS 1.26:193120  Unknown 
 Kreinovich, Vladik.
 [Washington, DC? : National Aeronautics and Space Administration ; Springfield, Va. : National Technical Information Service, distributor, 1992]
 Description
 Book — 1 v.
 Online
Green Library
Green Library  Status 

Find it US Federal Documents  
NAS 1.26:192952  Unknown 
8. Strongly transitive fuzzy relations [microform] : a more adequate way to describe similarity [1992]
 Kreinovich, Vladik.
 [Washington, DC? : National Aeronautics and Space Administration ; Springfield, Va. : National Technical Information Service, distributor, 1992]
 Description
 Book — 1 v.
 Online
Green Library
Green Library  Status 

Find it US Federal Documents  
NAS 1.26:192951  Unknown 
 Kreinovich, Vladik.
 [Washington, DC? : National Aeronautics and Space Administration ; Springfield, Va. : National Technical Information Service, distributor, 1991]
 Description
 Book — 1 v.
 Online
Green Library
Green Library  Status 

Find it US Federal Documents  
NAS 1.26:192947  Unknown 
 Kreinovich, Vladik.
 [Washington, DC? : National Aeronautics and Space Administration ; Springfield, Va. : National Technical Information Service, distributor, 1991]
 Description
 Book — 1 v.
 Online
Green Library
Green Library  Status 

Find it US Federal Documents  
NAS 1.26:192948  Unknown 
 Bokati, Laxman, 1989 author.
 Cham, Switzerland : Springer, 2023.
 Description
 Book — 1 online resource (xi, 210 pages) : illustrations (black and white).
 Summary

 Intro
 Contents
 Part I Introduction
 1 General Introduction
 2 (Rational) Individual Decision Making: Main Ideas
 References
 3 (Rational) Group Decision Making: General Formulas and a New Simplified Derivation of These Formulas
 3.1 (Rational) Group Decision Making: General Formulas
 3.2 A New (Simplified) Explanation of Nash's Bargaining Solution
 3.3 Taking Empathy into Account
 References
 4 How We Can Control Group Decision Making by Modifying the Proposed Options
 4.1 Formulation of the Problem
 4.2 Main Idea and the Resulting Explanation
 4.3 Proof of the Main Result
 References
 Part II How People Actually Make Decisions
 5 The Fact That We Can Only Have Approximate Estimates Explains Why Buying and Selling Prices are Different
 5.1 People's Actual Decisions Often Differ from What Decision Theory Recommends
 5.2 Buying and Selling Prices are Different: A Phenomenon and Its Current Quantitative Explanations
 5.3 A New (Hopefully, More Adequate) Quantitative Explanation
 References
 6 The ``No Trade Theorem'' Paradox
 6.1 ``No Trade Theorem'' and Why It is a Paradox
 6.2 Analysis of the Problem and the Resulting Explanation of the ``No Trade Theorem'' Paradox
 6.3 Auxiliary Result: Decision Theory Explains Why Depressed People are More RiskAverse
 References
 7 People Make Decisions Based on Clusters Containing Actual Values
 7.1 Formulation of the Problem
 7.2 A Possible Geometric Explanation
 7.3 Auxiliary Observation: How all This is Related to Our Understanding of Directions
 References
 8 When Revolutions Succeed
 8.1 Formulation of the Problem
 8.2 80/20 Rule: Reminder
 8.3 How These Two Laws Explain the 3.5% Rule
 References
 9 How People Combine Utility Values
 9.1 Common Sense Addition
 9.2 Towards Precise Formulation of the Problem
 9.3 Hurwicz OptimismPessimism Criterion: Reminder
 9.4 Analysis of the Problem and the Resulting Explanation of Common Sense Addition
 References
 10 Biased Perception of Time
 10.1 Formulation of the Problem
 10.2 How Decision Theory Can Explain the Telescoping Effect
 References
 11 Biased Perception of Future Time Leads to NonOptimal Decisions
 References
 12 People Have Biased Perception of Other People's Utility
 References
 13 People Select Approximately Optimal Alternatives
 13.1 People Use Softmax Instead of Optimization
 13.2 Problem: Need to Generalize Softmax to the Case of Interval Uncertainty
 13.3 How to Generalize: The Proposed Solution
 References
 14 People Make Decisions Using Heuristics. I
 14.1 Formulation of the Problem
 14.2 Case When We Only Know the Expected Rates of Return ...
 14.3 Case When We Only Know the Intervals Containing the Actual ...
 References
 15 People Make Decisions Using Heuristics. II
 15.1 Formulation of the Problem
 Urenda, Julio C., author.
 Cham : Springer, 2022.
 Description
 Book — 1 online resource : illustrations (black and white, and color).
 Summary

 Introduction. What Are the Most Natural and the Most Frequent Transformations. Which Functions and Which Families of Functions Are Invariant. What Is the General Relation Between Invariance And Optimality. General Application: Dynamical Systems. First Application to Physics: Why Liquids?. Second Application to Physics: Warping of Our Galaxy.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Cham : Springer, ©2021.
 Description
 Book — 1 online resource (xix, 265 pages) : illustrations (some color)
 Summary

 Vitae of Hung T. Nguyen
 Almost half a century after our encounter with the theory of information proposed by Joseph Kampé de Fériet
 An Enjoyable Research Journey on Uncertainty
 A Bayesian Dilemma
 The Fell compactification of a poset.
 Cham, Switzerland : Springer, [2019]
 Description
 Book — 1 online resource Digital: text file.PDF.
 Summary

This book presents recent research on probabilistic methods in economics, from machine learning to statistical analysis. Economics is a very important  and at the same a very difficult discipline. It is not easy to predict how an economy will evolve or to identify the measures needed to make an economy prosper. One of the main reasons for this is the high level of uncertainty: different difficulttopredict events can influence the future economic behavior. To make good predictions and reasonable recommendations, this uncertainty has to be taken into account. In the past, most related research results were based on using traditional techniques from probability and statistics, such as pvaluebased hypothesis testing. These techniques led to numerous successful applications, but in the last decades, several examples have emerged showing that these techniques often lead to unreliable and inaccurate predictions. It is therefore necessary to come up with new techniques for processing the corresponding uncertainty that go beyond the traditional probabilistic techniques. This book focuses on such techniques, their economic applications and the remaining challenges, presenting both related theoretical developments and their practical applications.
(source: Nielsen Book Data)
15. Combining interval, probabilistic, and other types of uncertainty in engineering applications [2018]
 Pownuk, Andrew M., 1969 author.
 Cham, Switzerland : Springer, 2018.
 Description
 Book — 1 online resource (xi, 202 pages) : illustrations (some color) Digital: text file.PDF.
 Summary

 Introduction. How to Get More Accurate Estimates. How to Speed Up Computations. Towards a Better Understandability of UncertaintyEstimating Algorithms. How General Can We Go: What Is Computable and What Is Not. Decision Making Under Uncertainty. Conclusions.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Servin, Christian, author.
 Cham : Springer, [2015]
 Description
 Book — 1 online resource : illustrations Digital: text file.PDF.
 Summary

 Introduction. Towards a More Adequate Description of Uncertainty. Towards Justification of Heuristic Techniques for Processing Uncertainty. Towards More Computationally Efficient Techniques for Processing Uncertainty. Towards Better Ways of Extracting Information About Uncertainty from Data.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Nava, Jaime, 1984 author.
 Heidelberg : Springer, [2014]
 Description
 Book — 1 online resource (viii, 155 pages) : illustrations Digital: text file.PDF.
 Summary

 Introduction: Symmetries and Similarities as a Methodology for Algorithmics of Analysis, Prediction, and Control in Science and Engineering. Algorithmic Aspects of RealLife Systems Analysis: Approach Based on Symmetry and Simila
 Algorithmic Aspects of Prediction: An Approach Based on Symmetry and Similarity
 Algorithmic Aspects of Control: Approach Based on Symmetry and Similarity
 Possible Ideas for FutureWork.
 Reznik, Leon.
 Berlin, Heidelberg : Springer Berlin Heidelberg, 2003.
 Description
 Book — 1 online resource (xiv, 284 pages)
 Summary

 Uncertainty in Measurement: Some Thoughts about its Expressing and Processing. Why Two Sigma? A Theoretical Justification for an Empirical Measurement Practice. Fuzzy Linguistic Scales: Definition, Properties and Applications. A Fuzzy Shape Specification System to Support Design for Aesthetics. Generating Membership Functions for a Noise Annoyance Model from Experimental Data. An Exegesis of Data Fusion. Possibilistic Logic: A Theoretical Framework for Multiple Source Information Fusion. Automated Adaptive Situation Assessment. Soft Computing, Realtime Measurement and Information Processing in a Modern Brewery. The Aggregation of Industrial Performance Information by the Choquet Fuzzy Integral. Computing Image with an Analog Circuit Inspired by the Outer Retinal Network. Extending the Decision Accuracy of a Bioinformatics System. On Fuzzy Controllers Having Radial Basis Transfer Functions. Evolutionary Scene Recognition and Simultaneous Position/Orientation Detection. Evolutionary Dynamics Identification of MultiLink Manipulators Using Runge  Kutta  Gill RBF Networks. Towards Reliable SubDivision of Geological Areas: Interval Approach. A Fuzzy Classifier with Pyramidal Membership Functions. A Comparison of Soft Computing and Traditional Approaches for Risk Classification and Claim Cost Prediction in the Automobile Insurance Industry. Evolutionary Rule Generation and its Application to Credit Scoring. Social Fuzziology in Action: Acquisition and Making Sense of Social Information.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Dordrecht ; Boston : Kluwer Academic Publishers, c1998.
 Description
 Book — xii, 459 p. ; 25 cm.
 Summary

 1. Informal Introduction: Data Processing, Interval Computations, and Computational Complexity. 2. The Notions of Feasibility and NPHardness: Brief Introduction. 3. In the General Case, The Basic Problem of Interval Computations is Intractable. 4. Basic Problem of Interval Computations for Polynomials of a Fixed Number of Variables. 5. Basic Problem of Interval Computations for Polynomials of Fixed Order. 6. Basic Problem of Interval Computations for Polynomials with Bounded Coefficients. 7. Fixed Data Processing Algorithms, Varying Data: Still NPHard. 8. Fixed Data, Varying Data Processing Algorithms: Still Intractable. 9. What if we Only Allow Some Arithmetic Operations in Data Processing? 10. For FractionallyLinear Functions, A Feasible Algorithm Solves the Basic Problem of Interval Computations. 11. Solving Interval Linear Systems is NPHard. 12. Interval Linear Systems: Search for Feasible Classes. 13. Physical Corollary: Prediction is Not Always Possible, Even for Linear Systems with Known Dynamics. 14. Engineering Corollary: Signal Processing is NPHard. 15. Bright Sides of NPHardness of Interval Computations I: NPHard Means that Good Interval Heuristics Can Solve Other Hard Problems. 16. If Input Intervals are Narrow Enough, then Interval Computations are Almost Always Easy. 17. Optimization
 A First Example of a Numerical Problem in Which Interval Methods are Used: Computational Complexity and Feasibility. 18. Solving Systems of Equations. 19. Approximation of Interval Functions. 20. Solving Differential Equations. 21. Properties of Interval Matrices I: Main Results. 22. Properties of Interval Matrices II: Proofs and Auxiliary Results. 23. NonInterval Uncertainty I: Ellipsoid Uncertainty And its Generalizations. 24. NonInterval Uncertainty II: MultiIntervals and Their Generalizations. 25. What if Quantities are Discrete? 26. Error Estimation for Indirect Measurements: Interval Computation Problem is (Slightly) Harder than a Similar Probabilistic Computational Problem. A: In Case of Interval (or More General) Uncertainty, No Algorithm can Choose the Simplest Representative. B: Error Estimation for Indirect Measurements: Case of Approximately Known Functions. C: From Interval Computations to Modal Mathematics. D: Beyond NP: Two Roots Good, One Root Better. E: Does `NPHard' Really Mean `Intractable'? F: Bright Sides of NPHardness of Interval Computations II: Freedom of Will? G: The Worse, the Better: Paradoxical Computational Complexity of Interval Computations and Data Processing.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
SAL3 (offcampus storage)
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QA267.7 .C68 1998  Available 
 Nguyen, Hung T., 1944
 Dordrecht ; Boston : Kluwer Academic, c1997.
 Description
 Book — xii, 419 p. : ill. ; 25 cm.
 Summary

 Algorithm Complexity: Two Simple Examples.
 2. Solving General Linear Functional Equations: An Application to Algorithm Complexity.
 3. Program Testing: A Problem.
 4. Optimal Program Testing.
 5. Optimal Choice of a Penalty Function: Simplest Case of Algorithm Design.
 6. Solving General Linear Differential Equations with Constant Coefficients: An Application to Constrained Optimization.
 7. Simulated Annealing: `Smooth' (Local) Discrete Optimization.
 8. Genetic Algorithms: `NonSmooth' Discrete Optimization.
 9. RISC Computer Architecture and Internet Growth: Two Applications of Extrapolation.
 10. Systems of Differential Equations and Their Use in ComputerRelated Extrapolation Problems.
 11. Network Congestion: An Example of NonLinear Extrapolation.
 12. Neural Networks: A General Form of NonLinear Extrapolation.
 13. Expert Systems and the Basics of Fuzzy Logic.
 14. Intelligent and Fuzzy Control.
 15. Randomness, Chaos, and Fractals. A: Simulated Annealing Revisited. B: Software Cost Estimation. C: Electronic Engineering: How to Describe PNJunctions. D: LogNormal Distribution Justified: An Application to Computational Statistics. E: Optimal Robust Statistical Methods. F: How to Avoid Paralysis of Neural Networks. G: Estimating Computer Prices. H: Allocating Bandwidth on Computer Networks. I: Algorithm Complexity Revisited. J: How Can a Robot Avoid Obstacles: Case Study of RealTime Optimization. K: Discounting in Robot Control: A Case Study of Dynamic Optimization. Index.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Online
SAL3 (offcampus storage)
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Stacks  Request (opens in new tab) 
QA76.9 .M35 N49 1997  Available 
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