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Most successful applications of modern science and engineering, from discovering the human genome to predicting weather to controlling space missions, involve processing large amounts of data and large knowledge bases. The ability of computers to perform fast data and knowledge processing is based on the hardware support for super-fast elementary computer operations, such as performing arithmetic operations with (exactly known) numbers and performing logical operations with binary ("true"-"false") logical values. In practice, measurements are never 100% accurate. It is therefore necessary to find out how this input inaccuracy (uncertainty) affects the results of data processing. Sometimes, we know the corresponding probability distribution; sometimes, we only know the upper bounds on the measurement error -- which leads to interval bounds on the (unknown) actual value. Also, experts are usually not 100% certain about the statements included in the knowledge bases. A natural way to describe this uncertainty is to use non-classical logics (probabilistic, fuzzy, etc.). This book contains proceedings of the first international workshop that brought together researchers working on interval and probabilistic uncertainty and on non-classical logics. We hope that this workshop will lead to a boost in the much-needed collaboration between the uncertainty analysis and non-classical logic communities, and thus, to better processing of uncertainty.

Uncertainty (Information theory) --- Nonclassical mathematical logic --- Incertitude (Théorie de l'information) --- Congresses --- Congrès --- Artificial intelligence. --- Engineering mathematics. --- Engineering. --- Mathematics. --- Statistics. --- Uncertainty (Information theory). --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Civil Engineering --- Operations Research --- Computer Science --- Applied Mathematics --- Mathematical logic, Nonclassical --- Non-classical mathematical logic --- Applied mathematics. --- Applications of Mathematics. --- Artificial Intelligence (incl. Robotics). --- Appl.Mathematics/Computational Methods of Engineering. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Engineering --- Engineering analysis --- Mathematical analysis --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Math --- Science --- Logic, Symbolic and mathematical --- Artificial Intelligence. --- Mathematical and Computational Engineering. --- Statistics .

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This book constitutes the refereed proceedings of the 8th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2020, held in Phuket, Thailand, in November 2020.* The 35 full papers presented were carefully reviewed and selected from 55 submissions. The papers deal with all aspects of uncertainty modelling and management and are organized in topical sections on uncertainty management and decision support; machine learning; machine learning applications; econometric applications; and statistical methods. * The conference was held virtually due to the COVID-19 pandemic.

Mathematics --- Electronics --- Telecommunication technology --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- optische communicatie --- optische elektronica --- computers --- informatica --- datacommunicatie --- externe fixatie (geneeskunde --- KI (kunstmatige intelligentie) --- AI (artificiële intelligentie) --- Uncertainty (Information theory)

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Solving practical problems often requires the integration of information and knowledge from many different sources, taking into account uncertainty and impreciseness. Typical situations are, for instance, when we need to simultaneously process both measurement data and expert knowledge, where the former may be uncertain and inaccurate due to randomness or error in measurements whilst the latter are often vague and imprecise due to a lack of information or human’s subjective judgements. This gives rise to the demand for methods and techniques of managing and integrating various types of uncertainty within a coherent framework, so as to ultimately improve the solution to any such complex problem in practice. The 2010 International Symposium on Integrated Uncertainty Management and Applications (IUM’2010), which takes place at the Japan Advanced Institute of Science and Technology (JAIST), Ishikawa, Japan, between 9th–11thApril, is therefore conceived as a forum for the discussion and exchange of research results, ideas for and experience of application among researchers and practitioners involved with all aspects of uncertainty modelling and management.

Soft computing -- Congresses. --- Uncertainty -- Mathematical models -- Congresses. --- Uncertainty (Information theory) -- Congresses. --- Soft computing --- Uncertainty --- Uncertainty (Information theory) --- Computer Science --- Engineering & Applied Sciences --- Mathematical models --- Uncertainty. --- Engineering. --- Artificial intelligence. --- Computational intelligence. --- Computational Intelligence. --- Artificial Intelligence (incl. Robotics). --- Intelligence, Computational --- Artificial intelligence --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Construction --- Industrial arts --- Technology --- Reasoning --- Artificial Intelligence.

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This book is devoted to the analysis of causal inference which is one of the most difficult tasks in data analysis: when two phenomena are observed to be related, it is often difficult to decide whether one of them causally influences the other one, or whether these two phenomena have a common cause. This analysis is the main focus of this volume. To get a good understanding of the causal inference, it is important to have models of economic phenomena which are as accurate as possible. Because of this need, this volume also contains papers that use non-traditional economic models, such as fuzzy models and models obtained by using neural networks and data mining techniques. It also contains papers that apply different econometric models to analyze real-life economic dependencies.

Computer Science --- Engineering & Applied Sciences --- Engineering. --- Economics, Mathematical. --- Computational intelligence. --- Quality control. --- Reliability. --- Industrial safety. --- Computational Intelligence. --- Quantitative Finance. --- Quality Control, Reliability, Safety and Risk. --- Industrial accidents --- Industries --- Job safety --- Occupational hazards, Prevention of --- Occupational health and safety --- Occupational safety and health --- Prevention of industrial accidents --- Prevention of occupational hazards --- Safety, Industrial --- Safety engineering --- Safety measures --- Safety of workers --- Accidents --- System safety --- Dependability --- Trustworthiness --- Conduct of life --- Factory management --- Industrial engineering --- Reliability (Engineering) --- Sampling (Statistics) --- Standardization --- Quality assurance --- Quality of products --- Intelligence, Computational --- Artificial intelligence --- Soft computing --- Economics --- Mathematical economics --- Econometrics --- Mathematics --- Construction --- Industrial arts --- Technology --- Prevention --- Methodology --- Finance. --- System safety. --- Safety, System --- Safety of systems --- Systems safety --- Industrial safety --- Systems engineering --- Funding --- Funds --- Currency question --- Economics, Mathematical . --- Econometrics. --- Economics, Mathematical --- Statistics

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In economics, many quantities are related to each other. Such economic relations are often much more complex than relations in science and engineering, where some quantities are independence, and the relation between others can be well approximated by linear functions. As a result of this complexity, when we apply traditional statistical techniques -- developed for science and engineering -- to process economic data, the inadequate treatment of dependence leads to misleading models and erroneous predictions. Some economists even blamed such inadequate treatment of dependence for the 2008 financial crisis. To make economic models more adequate, we need more accurate techniques for describing dependence. Such techniques are currently being developed. This book contains description of state-of-the-art techniques for modeling dependence, and economic applications of these techniques. Most of these research developments are centered around the notion of a copula -- a general way of describing dependence in probability theory and statistics. To be even more adequate, many papers go beyond traditional copula techniques and take into account, e.g., the dynamical (changing) character of the dependence in economics.

Artificial intelligence. --- Econometrics. --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Engineering. --- Computational intelligence. --- Computational Intelligence. --- Artificial Intelligence (incl. Robotics). --- Economics, Mathematical --- Statistics --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Intelligence, Computational --- Artificial intelligence --- Soft computing --- Construction --- Industrial arts --- Technology --- Artificial Intelligence. --- Econometric models. --- Econometrics --- Mathematical models.