How Uncertainty-Related Ideas Can Provide Theoretical Explanation For Empirical Dependencies PDF Download
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Author: Martine Ceberio
Publisher: Springer Nature
ISBN: 3030653242
Category : Technology & Engineering
Languages : en
Pages : 152
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Book Description
This book shows how to provide uncertainty-related theoretical justification for empirical dependencies, on the examples from numerous application areas. Such justifications are needed, since without them, practitioners may be reluctant to use these dependencies: purely empirical formulas often turn out to hold only in some cases. Examples of new theoretical explanations range from fundamental physics (quark confinement, galaxy superclusters, etc.) and geophysics (earthquake analysis) to transportation and electrical engineering to computer science (image processing, quantum computing) and pedagogy (equity, effect of repetitions). The book is useful to students and specialists in the corresponding areas. Most of the examples use common general techniques, so the book is also useful to practitioners and researchers in other application areas who look for ways to provide theoretical justifications for their areas’ empirical dependencies.
Author: Martine Ceberio
Publisher: Springer Nature
ISBN: 3030653242
Category : Technology & Engineering
Languages : en
Pages : 152
Get Book Here
Book Description
This book shows how to provide uncertainty-related theoretical justification for empirical dependencies, on the examples from numerous application areas. Such justifications are needed, since without them, practitioners may be reluctant to use these dependencies: purely empirical formulas often turn out to hold only in some cases. Examples of new theoretical explanations range from fundamental physics (quark confinement, galaxy superclusters, etc.) and geophysics (earthquake analysis) to transportation and electrical engineering to computer science (image processing, quantum computing) and pedagogy (equity, effect of repetitions). The book is useful to students and specialists in the corresponding areas. Most of the examples use common general techniques, so the book is also useful to practitioners and researchers in other application areas who look for ways to provide theoretical justifications for their areas’ empirical dependencies.
Author: Van-Nam Huynh
Publisher: Springer Nature
ISBN: 3031467752
Category : Computers
Languages : en
Pages : 351
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Book Description
These two volumes constitute the proceedings of the 10th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2023, held in Kanazawa, Japan, during November 2-4, 2023. The 58 full papers presented were carefully reviewed and selected from 107 submissions. The papers deal with all aspects of research results, ideas, and experiences of application among researchers and practitioners involved with all aspects of uncertainty modelling and management.
Author: Laxman Bokati
Publisher: Springer Nature
ISBN: 3031260864
Category : Technology & Engineering
Languages : en
Pages : 203
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Book Description
This book describes new techniques for making decisions in situations with uncertainty and new applications of decision-making techniques. The main emphasis is on situations when it is difficult to decrease uncertainty. For example, it is very difficult to accurately predict human economic behavior, so in economics, it is very important to take this uncertainty into account when making decisions. Other areas where it is difficult to decrease uncertainty are geosciences and teaching. The book analyzes the general problem of decision making and shows how its results can be applied to economics, geosciences, and teaching. Since all these applications involve computing, the book also shows how these results can be applied to computing, including deep learning and quantum computing. The book is recommended to researchers, practitioners, and students who want to learn more about decision making under uncertainty—and who want to work on remaining challenges.
Author: Martine Ceberio
Publisher: Springer Nature
ISBN: 3031164156
Category : Technology & Engineering
Languages : en
Pages : 286
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Book Description
This book shows, on numerous examples, how to make decisions in realistic situations when we have both uncertainty and constraints. In most these situations, the book's emphasis is on the why-question, i.e., on a theoretical explanation for empirical formulas and techniques. Such explanations are important: they help understand why these techniques work well in some cases and not so well in others, and thus, help practitioners decide whether a technique is appropriate for a given situation. Example of applications described in the book ranges from science (biosciences, geosciences, and physics) to electrical and civil engineering, education, psychology and decision making, and religion—and, of course, include computer science, AI (in particular, eXplainable AI), and machine learning. The book can be recommended to researchers and students in these application areas. Many of the examples use general techniques that can be used in other application areas as well, so it is also useful for practitioners and researchers in other areas who are looking for possible theoretical explanations of empirical formulas and techniques.
Author: Isaac Elishakoff
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3111354237
Category : Technology & Engineering
Languages : en
Pages : 384
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Book Description
The book exposes three alternative and competing approaches to uncertainty analysis in engineering. It is composed of some essays on various sub-topics like random vibrations, probabilistic reliability, fuzzy-sets-based analysis, unknown-but-bounded variables, stochastic linearization, possible difficulties with stochastic analysis of structures.
Author: Julio C. Urenda
Publisher: Springer Nature
ISBN: 3031167805
Category : Computers
Languages : en
Pages : 246
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Book Description
The book explores a new general approach to selecting—and designing—data processing techniques. Symmetry and invariance ideas behind this algebraic approach have been successful in physics, where many new theories are formulated in symmetry terms. The book explains this approach and expands it to new application areas ranging from engineering, medicine, education to social sciences. In many cases, this approach leads to optimal techniques and optimal solutions. That the same data processing techniques help us better analyze wooden structures, lung dysfunctions, and deep learning algorithms is a good indication that these techniques can be used in many other applications as well. The book is recommended to researchers and practitioners who need to select a data processing technique—or who want to design a new technique when the existing techniques do not work. It is also recommended to students who want to learn the state-of-the-art data processing.
Author: Mykel J. Kochenderfer
Publisher: MIT Press
ISBN: 0262331713
Category : Computers
Languages : en
Pages : 350
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Book Description
An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.
Author: Jeffrey Pfeffer
Publisher: Stanford University Press
ISBN: 080474789X
Category : Business & Economics
Languages : en
Pages : 336
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Book Description
This work explores how external constraints affect organizations and provides insights for designing and managing organizations to mitigate these constraints. All organizations are dependent on the environment for their survival. It contends that it is the fact of the organization's dependence on the environment that makes the external constraint and control of organizational behaviour both possible and almost inevitable. Organizations can either try to change their environments through political means or form interorganizational relationships to control or absorb uncertainty.
Author: Michael Waldmann
Publisher: Oxford University Press
ISBN: 0199399565
Category : Psychology
Languages : en
Pages : 769
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Book Description
Causal reasoning is one of our most central cognitive competencies, enabling us to adapt to our world. Causal knowledge allows us to predict future events, or diagnose the causes of observed facts. We plan actions and solve problems using knowledge about cause-effect relations. Although causal reasoning is a component of most of our cognitive functions, it has been neglected in cognitive psychology for many decades. The Oxford Handbook of Causal Reasoning offers a state-of-the-art review of the growing field, and its contribution to the world of cognitive science. The Handbook begins with an introduction of competing theories of causal learning and reasoning. In the next section, it presents research about basic cognitive functions involved in causal cognition, such as perception, categorization, argumentation, decision-making, and induction. The following section examines research on domains that embody causal relations, including intuitive physics, legal and moral reasoning, psychopathology, language, social cognition, and the roles of space and time. The final section presents research from neighboring fields that study developmental, phylogenetic, and cultural differences in causal cognition. The chapters, each written by renowned researchers in their field, fill in the gaps of many cognitive psychology textbooks, emphasizing the crucial role of causal structures in our everyday lives. This Handbook is an essential read for students and researchers of the cognitive sciences, including cognitive, developmental, social, comparative, and cross-cultural psychology; philosophy; methodology; statistics; artificial intelligence; and machine learning.
Author: Marieka Brouwer Burg
Publisher: Springer
ISBN: 3319278339
Category : Social Science
Languages : en
Pages : 189
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Book Description
This volume deals with the pressing issue of uncertainty in archaeological modeling. Detecting where and when uncertainty is introduced to the modeling process is critical, as are strategies for minimizing, reconciling, or accommodating such uncertainty. Included chapters provide unique perspectives on uncertainty in archaeological modeling, ranging in both theoretical and methodological orientation. The strengths and weaknesses of various identification and mitigation techniques are discussed, in particular sensitivity analysis. The chapters demonstrate that for archaeological modeling purposes, there is no quick fix for uncertainty; indeed, each archaeological model requires intensive consideration of uncertainty and specific applications for calibration and validation. As very few such techniques have been problematized in a systematic manner or published in the archaeological literature, this volume aims to provide guidance and direction to other modelers in the field by distilling some basic principles for model testing derived from insight gathered in the case studies presented. Additionally, model applications and their attendant uncertainties are presented from distinct spatio-temporal contexts and will appeal to a broad range of archaeological modelers. This volume will also be of interest to non-modeling archaeologists, as consideration of uncertainty when interpreting the archaeological record is also a vital concern for the development of non-formal (or implicit) models of human behavior in the past.
