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Combining Interval Probabilistic and Other Types of Uncertainty in Engineering

uncertainty: interval, probabilistic, fuzzy, etc.? This book provides a theoretical basis for arriving at such solutions, as well as case studies;

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Beyond Traditional Probabilistic Data Processing Techniques

; this is known as fuzzy uncertainty. In this book, leading specialists on interval, fuzzy, probabilistic uncertainty and their combination;

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Neutron Diffusion

This book is designed for a systematic understanding of nuclear diffusion theory along with fuzzyinterval/stochastic uncertainty. This;

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Engineering Design Reliability Handbook

non-deterministic approaches including probabilistic, interval and fuzzy sets-based methods, generalized information theory, Dempster-Shaffer;

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Uncertainty Modeling and Analysis in Engineering and the Sciences

of uncertainty and a reliance on expert opinions. Uncertainty Modeling and Analysis in Engineering and the Sciences prepares current and future analysts;

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Bayesian Inverse Problems

Gain a fundamental understanding of the use of probabilistic methods and reliability analysis techniques in engineering applications;

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Dimensions of Uncertainty in Communication Engineering

Dimensions of Uncertainty in Communication Engineering is a comprehensive and self-contained introduction to the problems;

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Uncertainty In Risk Assessment

Explores methods for the representation and treatment of uncertainty in risk assessment In providing guidance for practical decision-making;

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Engineering Design Under Uncertainty and Health Prognostics

This book presents state-of-the-art probabilistic methods for the reliability analysis and design of engineering products and processes. It;

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Probabilistic Reasoning in Intelligent Systems

with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth;

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Measurement Uncertainty And Probability

uncertainty', and how such an interval can be calculated. The book argues that the concept of an unknown 'target value' is essential if probability;

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Decision Making under Constraints

algorithms and making these algorithms as efficient as possible; and (4) taking into account interval, probabilistic, and fuzzy uncertainty inherent;

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Decision Making under Constraints

algorithms and making these algorithms as efficient as possible; and (4) taking into account interval, probabilistic, and fuzzy uncertainty inherent;

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Fuzzy Dynamic Equations, Dynamic Inclusions, and Optimal Control Problems on Time Scales

, engineering and technology. In some cases, there exists uncertainty, ambiguity, or vague factors in such problems, and fuzzy theory and interval;

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Analyzing Uncertainty in Civil Engineering

, shortcomings of a strictly probabilistic safety concept, geotechnical models of failure mechanisms and their implications forconstruction mana- ment;

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Cognition and Chance

probabilistic naivete, impairs decision making under uncertainty, facilitates the misinterpretation of statistical information, and precludes critical;

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Extension of the Fuzzy Sugeno Integral Based on Generalized Type-2 Fuzzy Logic

fuzzy logic. This extension enables it to handle higher levels of uncertainty when adding any number of sources and types of information in a;

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Reliability and Safety Engineering

uncertainty, so the book includes uncertainty propagation methods: Monte Carlo simulation, fuzzy arithmetic, Dempster-Shafer theory and probability;

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Recent Advances in Applications of Computational and Fuzzy Mathematics

This book addresses the basics of interval/fuzzy set theory, artificial neural networks (ANN) and computational methods. It presents step;

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Recent Advances in Applications of Computational and Fuzzy Mathematics

This book addresses the basics of interval/fuzzy set theory, artificial neural networks (ANN) and computational methods. It presents step;

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Therapeutic Potential of Furin Inhibition

, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty;

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Statistical Relational Artificial Intelligence

, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty;

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Statistical Relational Artificial Intelligence

, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty;

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Statistical Relational Artificial Intelligence

, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty;

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Probabilistic Forecasting and Bayesian Data Assimilation

probabilistic forecasting and uncertainty quantification. Part II is devoted to Bayesian filtering algorithms, from classical data assimilation;

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Probabilistic Forecasting and Bayesian Data Assimilation

probabilistic forecasting and uncertainty quantification. Part II is devoted to Bayesian filtering algorithms, from classical data assimilation;

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Algorithms for Optimization

surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation;

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