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From intervals to -? : towards a general description of validated uncertainty / / Vladik Kreinovich, Graçaliz Pereira Dimuro, Antônio Carlos da Rocha Costa
| From intervals to -? : towards a general description of validated uncertainty / / Vladik Kreinovich, Graçaliz Pereira Dimuro, Antônio Carlos da Rocha Costa |
| Autore | Kreinovich Vladik |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (125 pages) |
| Disciplina | 006.3 |
| Collana | Studies in computational intelligence |
| Soggetto topico |
Computational intelligence
Uncertainty (Information theory) |
| ISBN | 3-031-20569-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- 1 Motivation and Outline -- 1.1 Why Computers? -- 1.2 Why Interval Computations? -- 1.3 Why Go Beyond Intervals? -- 1.4 Outline -- References -- 2 A General Description of Measuring Devices: Plan -- 3 A General Description of Measuring Devices: First Step-Finite Set of Possible Outcomes -- 3.1 Every Measuring Device Has Finitely Many Possible Outcomes -- 3.2 Not All Marks on a Scale Can Be Physically Possible -- 3.3 We Need a Theory -- 3.4 We Need a Theory that Also Described a Measuring Device -- 3.5 We Want a Theory that Is ``Full'' in Some Natural Sense -- 3.6 A Seemingly Natural Definition of a Full Theory is Not Always Adequate -- 3.7 What Exactly Is a Theory? -- 3.8 What Kind of Statements Are We Allowing? -- 3.9 What Exactly Is a Full Theory -- 3.10 The Existence of a Full Theory Makes the Set of All Physically … -- 3.11 Conclusion: Algorithmically Listable Set of Physically Possible Outcomes -- 3.12 Example 1: Interval Uncertainty -- 3.13 Example 2: Counting -- 3.14 Example 3: ``Yes''-``No'' Measurements -- 3.15 Example 3a: Repeated ``Yes''-``No'' Measurements -- 3.16 Example 4: A Combination of Several Independent Measuring Instruments -- References -- 4 A General Description of Measuring Devices: Second Step-Pairs of Compatible Outcomes -- 4.1 How Do We Describe Uncertainty: Main Idea -- 4.2 Comment on Quantum Measurements -- 4.3 Some Pairs of Outcomes Are Compatible (Close), Some Are Not -- 4.4 The Existence of a Full Theory Makes the Set of All Compatible Pairs of Outcomes Algorithmically Listable -- 4.5 Conclusion: Algorithmically Listable Set of Compatible Pairs of Outcomes -- 4.6 Description in Terms of Existing Mathematical Structures -- 4.7 Example 1: Interval Uncertainty -- 4.8 Example 2: Counting -- 4.9 Example 3: ``Yes''-``No'' Measurements.
4.10 Example 3a: Repeated ``Yes''-``No'' Measurements -- 4.11 Example 4: A Combination of Several Independent Measuring Instruments -- 4.12 Computational Complexity of the Graph Representation of a Measuring Device: General Case -- 4.13 Computational Complexity of the Graph Representation of a Measuring Device: Case of the Simplest Interval Uncertainty -- 4.14 Computational Complexity of the Graph Representation of a Measuring Device: General Case of Interval Uncertainty -- 4.15 Computational Complexity of the Graph Representation of a Measuring Device: Lower Bound for the Case of the General Interval Uncertainty -- 4.16 Computational complexity of the Graph Representation of a Measuring Device: Case of Multi-D Uncertainty -- 4.17 Computational Complexity of the Graph Representation of a Measuring Device: General Case of Localized Uncertainty -- References -- 5 A General Description of Measuring Devices: Third Step-Subsets of Compatible Outcomes -- 5.1 From Pairs to Subsets -- 5.2 Is Information About Compatible Pairs Sufficient? -- 5.3 Information About Compatible Pairs Is Sufficient For Intervals -- 5.4 Information About Compatible Pairs is Not Sufficient in the General Case -- 5.5 The Existence of a Full Theory Makes the Family of All Compatible … -- 5.6 Conclusion: Algorithmically Listable Family of Compatible Sets of Outcomes -- 5.7 Description in Terms of Existing Mathematical Structures: Simplicial Complexes -- 5.8 Resulting Geometric Representation of a Measuring Device -- 5.9 Towards Description in Terms of Existing Mathematical Structures: Domains -- 5.10 How to Reformulate the Above Description of a Measuring Device in Terms of Domains? -- 5.11 Example 1: Interval Uncertainty -- 5.12 Example 2: Counting -- 5.13 Example 3: ``Yes''-``No'' Measurements -- 5.14 Example 4: A Combination of Several Independent Measuring Instruments. 5.15 Computational Complexity of the Simplicial Complex Representation … -- 5.16 Computational Complexity of the Simplicial Complex Representation of a Measuring Device: Case of Interval Uncertainty -- 5.17 Computational Complexity of the Simplicial Complex Representation of a Measuring Device: Case of Multi-D Uncertainty -- 5.18 Computational Complexity of the Simplicial Complex Representation of a Measuring Device: General Case of Localized Uncertainty -- References -- 6 A General Description of Measuring Devices: Fourth Step-Conditional Statements About Possible Outcomes -- 6.1 Subsets of Compatible Outcomes Do Not Always Give A Complete Description of a Measuring Device -- 6.2 What We Do We Need to Add to the Subsets Description to Capture the Missing Information About a Measuring Device? -- 6.3 The Existence of a Full Theory Makes the Set of All True Conditional Statements Algorithmically Listable: An Argument -- 6.4 Family of Conditional Statements: Natural Properties -- 6.5 Conclusion: Algorithmically Listable Family of Conditional Statements -- 6.6 Description in Terms of Existing Mathematical Structures: Deduction Relation -- 6.7 Description in Terms of Existing Mathematical Structures: Domains -- 6.8 Example 1: Interval Uncertainty -- 6.9 Example 2: Counting -- 6.10 Example 3: ``Yes''-``No'' Measurements -- 6.11 Example 4: A Combination of Several Independent Measuring Instruments -- 6.12 Computational Complexity of the Domain Representation of a Measuring Device: A General Case -- 6.13 Computational Complexity of the Domain Representation of a Measuring Device: Case of Interval Uncertainty -- 6.14 Computational Complexity of the Simplicial Complex Representation of a Measuring Device: Case of Convex Multi-D Uncertainty. 6.15 Computational Complexity of the Domain Representation of a Measuring Device: General Case of Localized Uncertainty -- References -- 7 A General Description of Measuring Devices: Fifth Step-Disjunctive Conditional Statements About the Possible Outcomes -- 7.1 Addition of Conditional Statements Does Not Always Lead to a Complete Description of a Measuring Device -- 7.2 What We Do We Need to Add to the Conditional Statements Description to Capture the Missing Information About a Measuring Device? -- 7.3 The Existence of a Full Theory Makes the Set of All True Disjunctive Conditional Statements Algorithmically Listable -- 7.4 Family of True Disjunctive Conditional Statements: Natural Properties -- 7.5 Conclusion: Algorithmically Listable Family of Disjunctive Conditional Statements -- 7.6 Description in Terms of Existing Mathematical Structures: Sequent Calculus -- 7.7 Description in Terms of Existing Mathematical Structures: Boolean Vectors -- 7.8 Example -- 7.9 Description in Terms of Existing Mathematical Structures: Boolean Algebra -- 7.10 Example -- 7.11 Description in Terms of Existing Mathematical Structures: Domains -- 7.12 Example -- 7.13 Is This a Final Description of Validated Uncertainty? -- 7.14 Example 1: Interval Uncertainty -- 7.15 Example 2: Counting -- 7.16 Example 3: ``Yes''-``No'' Measurements -- 7.17 Example 4: A Combination of Several Independent Measuring Instruments -- 7.18 Computational Complexity of the Boolean Representation of a Measuring Device: A General Case -- 7.19 Computational Complexity of the Boolean Representation of a Measuring Device: Case of Interval Uncertainty -- 7.20 Computational Complexity of the Boolean Representation of a Measuring Device: Case of Convex Multi-D Uncertainty -- 7.21 Computational Complexity of the Domain Representation of a Measuring Device: General Case of Localized Uncertainty. References -- 8 A General Description of Measuring Devices: Summary -- 8.1 Summary -- 8.2 Measuring Device: A Final Description -- 9 Physical Quantities: A General Description -- 9.1 General Idea -- 9.2 From the General Idea to a Formal Description -- 9.3 Set of Possible Outcomes: The Notion of a Projection -- 9.4 Pairs of Compatible Outcomes: The Notion of a Projection -- 9.5 Subsets of Compatible Outcomes: The Notion of a Projection -- 9.6 Definition Reformulated in Domain Terms -- 9.7 General Domains and Boolean Vectors: The Notion of a Projection -- 9.8 The Family of All Measuring Devices Measuring A Given … -- 9.9 Physical Quantity as a Projective Limit of Measuring Devices -- 9.10 Example -- 9.11 Within This Definition, The Fact that Every Outcome … -- 9.12 Different Sequences of Measurement Results May Correspond to the Same Value of the Measured Quantity -- 9.13 Case of Graphs -- 9.14 Within This Definition, The Fact that simI Describes Exactly Compatible Pairs Is Now a Theorem -- 9.15 Case of Simplicial Complexes -- 9.16 Within This Definition, The Fact that calSI Describes Exactly Compatible Subsets Is Now A Theorem -- 9.17 Cases of Conditional Statements and Boolean Vectors -- 9.18 Examples: A Brief Introduction -- 9.18.1 Example 1: Interval Uncertainty Leads to Real Numbers -- 9.19 Conclusion -- 9.20 Example 2: Counting Leads to Natural Numbers -- 9.21 Example 3: ``Yes''-``No'' Measurements Lead to Truth Values -- 9.22 Example 4: A Combination of Several Independent Physical Quantities -- References -- 10 Properties of Physical Quantities -- 10.1 A Useful Auxiliary Result: We Can Always Restrict Ourselves to a Sequence of Measuring Devices -- 10.1.1 From the Physical Viewpoint, It is Important to Consider the Most General Families of Measuring Devices. 10.1.2 From the Purely Mathematical Viewpoint (of Proving Results), it is Desirable to Consider the Simpler Case of Sequences. |
| Record Nr. | UNINA-9910632469303321 |
Kreinovich Vladik
|
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| Cham, Switzerland : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Machine Learning for Econometrics and Related Topics
| Machine Learning for Econometrics and Related Topics |
| Autore | Kreinovich Vladik |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer, , 2024 |
| Descrizione fisica | 1 online resource (491 pages) |
| Disciplina | 330.015195 |
| Altri autori (Persone) |
SriboonchittaSongsak
YamakaWoraphon |
| Collana | Studies in Systems, Decision and Control Series |
| ISBN |
9783031436017
9783031436000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- An Invitation to Wasserstein Distance in Financial Risk Measures -- 1 Introduction -- 2 What Is the Wasserstein Distance? -- 3 General Wasserstein Metrics -- 4 Generalities on Risk Measures -- 5 Wasserstein Distance in Risk Analysis -- References -- What Makes a Good Model? -- 1 The World's Simplest Model -- 2 What Is a Model? -- 3 Model Goodness -- 3.1 Model Elements -- 3.2 Predictions -- 4 Conclusion -- References -- Quantifying Fairness and Discrimination in Predictive Models -- 1 Introduction -- 1.1 Sensitive Attribute and Discrimination -- 1.2 Examples of Discrimination -- 1.3 Notations on Classifiers -- 1.4 Agenda -- 2 Fairness by Unawareness -- 3 Group-Level Fairness -- 3.1 Independence and Conditional Independence -- 3.2 Demographic Parity -- 3.3 Equalized Odds (and Other Related Concepts) -- 3.4 Conditional Demographic Parity -- 3.5 Class Balance and Calibration -- 3.6 Principle of Non-reconstruction -- 3.7 Comparison of Fairness Criteria -- 3.8 Relaxation and Confidence Intervals -- 3.9 Implementation and Comparison -- 4 Individual-Level Fairness -- 4.1 Lipschitz Property -- 4.2 Counterfactual Fairness -- 5 Correcting Discrimination -- 5.1 Pre-processing Approaches -- 5.2 Reprocessing or In-Processing Algorithms -- 5.3 Post-processing -- References -- Slow-Growing Trees -- 1 Introduction -- 2 Ensembling Trees Revisited -- 2.1 What Is SGT Estimating? -- 2.2 Interpretation? -- 2.3 Hyperparameters and Computational Aspects -- 2.4 Related Works -- 2.5 Booging -- 3 Simulations -- 4 Empirics -- 5 Conclusion -- References -- Metrics on Probability Distributions Through Optimal Commuting Maps -- 1 Introduction -- 2 Wasserstein Distance and the Optimal Transport Problem -- 3 The nuν-Based Wasserstein Distance and Optimal Commuting Couplings -- References.
Using Machine Learning Methods to Estimate the Gender Wage Gap -- 1 Introduction -- 2 The Gender Wage Gap -- 3 Post-Double-Selection -- 4 Double-Debiased Machine Learning (DDML) -- 5 Data -- 6 Results -- 7 Conclusion -- References -- Forecasting Market Index of Stock Exchange of Thailand, Malaysia, and Singapore with the Gaussian Process Regression Model -- 1 Introduction -- 2 Gaussian Process Regression Model (GPR) -- 2.1 Autoregressive Model (AR(p)) -- 2.2 Transform Data to Rate of Return -- 2.3 Performance Evaluation -- 3 Real Data Application Using a Stock Market Index from Malaysia (KLSE), Singapore (STI), and Thailand (SET) -- 4 Forecasting Results -- 5 Conclusions and Future Research -- References -- The a Priori Procedure (APP) for Estimating Regression Coefficients in Multiple Linear Model with Skew Normal Errors -- 1 Introduction -- 2 Multivariate Skew Normal Distribution -- 3 Review of Multiple Linear Regression Model -- 4 The Necessary Sample Size Needed for Estimating the Vector of Regression Coefficients in Multiple Linear Model with Skew Errors -- 5 Simulation Study -- 6 Real Data Example -- 7 Discussion -- References -- Why Rectified Linear Unit Is Efficient in Machine Learning: One More Explanation -- 1 Formulation of the Problem -- 2 Our Main Idea and the Resulting Explanation -- References -- Why Shapley Value and Its Variants Are Useful in Machine Learning (and in Other Applications) -- 1 Formulation of the Problem -- 2 A New Derivation of Shapley Value (and Its Variants) -- References -- A Possible Common Mechanism Behind Skew Normal Distributions in Economics and Hydraulic Fracturing-Induced Seismicity -- 1 Formulation of the Problem -- 2 Hydraulic Fracturing-Induced Seismicity: First-Approximation Description and the Resulting Explanation -- References -- Why Quantile Regression Works Well in Economics: A Partial Explanation. 1 Formulation of the Problem -- 2 Case Study and the Resulting Partial Explanation -- References -- Trade with Heterogeneous Firms, Distance, and Time: An Analysis of Latin American and the Caribbean (LAC) Manufacturing Firms -- 1 Introduction -- 2 Literature Review -- 2.1 Theoretical Motivations for Heterogeneous Firms in Trade -- 2.2 The Trade Model with Heterogeneous Firms -- 3 The Data -- 3.1 Stylized Facts Based on the Data -- 4 Methodology -- 5 Results and Discussion -- 6 Conclusion -- Appendix 1 -- References -- Analysis and Prediction of Suitable Model for Coconut Production Estimates in South Indian States -- 1 Introduction -- 2 Materials and Methods -- 3 Results and Discussion -- 4 Conclusion -- References -- Portfolio Management of SET50 Stocks Using Deep Reinforcement Learning Methods -- 1 Introduction -- 2 Literature Review -- 3 Methods -- 3.1 Reinforcement Learning and Markov Decision Process (MDP) -- 3.2 Stock Market Environment -- 3.3 Trading Agent Based on Deep Reinforcement Learning -- 4 Empirical Results -- 5 Conclusion -- References -- Household Characteristics and the Pattern of Gambling, Alcohol and Tobacco Expenditures -- 1 Introduction -- 2 Data -- 3 Methodology -- 4 Results and Discussion -- 4.1 Cluster Analysis -- 4.2 Characteristics of Households in Different Clusters -- 5 Conclusion -- References -- Effects of Household Income and Parental Absence on Investment in Child Education in Thailand: Evidence from Quantile-on-Quantile Approach -- 1 Introduction -- 2 Data -- 3 Methodology -- 4 Results and Discussion -- 4.1 The Households' Expenditures on Child Education in Thailand -- 4.2 Income Elasticities of Educational Expenditure -- 4.3 Income Elasticities of Educational Expenditures by Presence of Parents -- 4.4 Heuristic Checking of the QQ Approach -- 5 Conclusion -- References. The Spatial Spillover Effects of Transportation Infrastructure on Economic Development in China -- 1 Introduction -- 2 Methodology and Data -- 2.1 Spatial Autocorrelation Test -- 2.2 Spatial Econometric Model -- 2.3 Data and Variables Description -- 3 Empirical Results -- 3.1 Results of Spatial Autocorrelation Test -- 3.2 Estimation Results -- 3.3 Results of Direct and Indirect Effects -- 4 Conclusions -- References -- Analyzing the Survival Probability of Community Enterprise Under the Situation of the COVID-19 Pandemic in Northern Thailand -- 1 Introduction -- 2 Methodology -- 2.1 The Cox Proportional Hazards Regression Model -- 3 Data Specification -- 4 Estimated Results -- 4.1 The Survival Probabilities of the Northern Community Enterprises Analyzed Using the COX Model -- 4.2 Survival Path Analysis -- 5 Conclusion -- References -- Bayesian Consideration for Trust in Ewom: Evidence from Vietnam -- 1 Introduction -- 2 Literature Review -- 2.1 Trust in eWOM (Electronic Word-Of-Mouth: Te) -- 2.2 Information Quality (IQ) -- 2.3 Care Information -- 2.4 Social Influence (SI) -- 2.5 Perceived Risk (PR) -- 3 Methodology -- 3.1 Sample Size -- 3.2 Bayes' Theorem -- 3.3 Bayes Inference -- 3.4 Selection of the Model by the Bayesian Model Averaging -- 3.5 Bayesian Information Criteria -- 4 Results -- 4.1 Reliability Test -- 4.2 BIC Algorithm -- 4.3 Model Evaluation -- 5 Conclusions -- References -- An Application of Explainable Artificial Intelligence in Credit Scoring -- 1 Introduction -- 2 Literature Review -- 2.1 Credit Scoring -- 2.2 Explainable Artificial Intelligence -- 3 Methodology and Data -- 3.1 Overview of Local Interpretable Model-Agnostic Explanations (LIME) -- 3.2 Mathematical Formulation of LIME -- 3.3 LIME in Detail -- 3.4 LIME Stability -- 3.5 Credit Scoring Using Random Forest (RF) -- 4 Empirical Results -- 4.1 German Credit Risk Dataset. 4.2 Taiwan Default Payment Dataset -- 5 Conclusion -- References -- Assessing Strategies for Adaption to Survive the COVID-19 Situation of OTOP Operators in Thailand -- 1 Introduction -- 2 Methodology -- 2.1 The Cox Proportional Hazards Model -- 2.2 Survival and Hazards Functions -- 3 Data -- 4 Empirical Results -- 4.1 Estimation Results of the Cox Proportional Hazards Model -- 4.2 Survival Path Analysis -- 5 Conclusion -- References -- The Effects of Oil Shocks on Inflation in Leading Crude Oil Importing Countries: Non-linear Autoregressive Distributed Lag -- 1 Introduction -- 2 Methodology and Data -- 2.1 Data -- 2.2 Methodology -- 3 Empirical Results -- 4 Conclusions -- References -- Contagion Effects Among Selected Asian Stock Markets During the COVID-19 Pandemic: A Dynamic Conditional Correlation Approach -- 1 Introduction -- 2 Methodology -- 2.1 GARCH-with-Jumps Model -- 2.2 DCC-GARCH -- 2.3 Copulas -- 3 Data -- 4 Estimation Results -- 5 Conclusion -- References -- The Nexus of the Nikkei 225, Gold, and Crude Oil. Do They Have a Co-movement in the Long Run? New Evidence for Cointegration from the Autoregressive Distributed Lag Bounds Test -- 1 Introduction -- 2 Literature Review -- 3 Data Descriptions and the Autoregressive Distributed Lag Approach for Cointegration -- 4 Empirical Results -- 4.1 The Frequentist Approach -- 4.2 The Bayesian Approach -- 5 Concluding Remarks -- References -- Does Contract Farming Improve Farmers' Income? The Case of Pineapple Farmers in Nong Khai and Loei, Thailand -- 1 Introduction -- 2 Methodology -- 2.1 Ridge Regression -- 2.2 Least Absolute Shrinkage and Selection Operator (LASSO) -- 2.3 Elastic Net -- 3 Data Description -- 4 Empirical Result -- 5 Conclusions and Policy Recommendations -- References -- Implications of Aging Population and Health Spending for Thai Economic Growth -- 1 Introduction. 2 Methodology. |
| Record Nr. | UNINA-9910865273403321 |
|
Kreinovich Vladik
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| Cham : , : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Towards Explainable Fuzzy AI: Concepts, Paradigms, Tools, and Techniques / / by Vladik Kreinovich
| Towards Explainable Fuzzy AI: Concepts, Paradigms, Tools, and Techniques / / by Vladik Kreinovich |
| Autore | Kreinovich Vladik |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (136 pages) |
| Disciplina | 006.3 |
| Collana | Studies in Computational Intelligence |
| Soggetto topico |
Computational intelligence
Artificial intelligence Engineering - Data processing Computational Intelligence Artificial Intelligence Data Engineering |
| ISBN |
9783031099748
9783031099731 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 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 Self-Test. |
| Record Nr. | UNINA-9910595058303321 |
|
Kreinovich Vladik
|
||
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||