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Quantification

Author: Anna Szabolcsi

Publisher: Cambridge University Press

Published: 2010-01-17

Total Pages: 265

ISBN-13: 113949158X

Quantification forms a significant aspect of cross-linguistic research into both sentence structure and meaning. This book surveys research in quantification starting with the foundational work in the 1970s. It paints a vivid picture of generalized quantifiers and Boolean semantics. It explains how the discovery of diverse scope behaviour in the 1990s transformed the view of quantification, and how the study of the internal composition of quantifiers has become central in recent years. It presents different approaches to the same problems, and links modern logic and formal semantics to advances in generative syntax. A unique feature of the book is that it systematically brings cross-linguistic data to bear on the theoretical issues, covering French, German, Dutch, Hungarian, Russian, Japanese, Telugu (Dravidian), and Shupamem (Grassfield Bantu) and points to formal semantic literature involving quantification in around thirty languages.

Interpretive Quantification

Author: J. Samuel Barkin

Publisher: University of Michigan Press

Published: 2017-01-27

Total Pages: 291

ISBN-13: 0472053396

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Revolutionary volume demonstrates how crossing the positivist and post-positivist divide improves political science research

Uncertainty Quantification and Predictive Computational Science

Author: Ryan G. McClarren

Publisher: Springer

Published: 2018-11-23

Total Pages: 345

ISBN-13: 3319995251

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This textbook teaches the essential background and skills for understanding and quantifying uncertainties in a computational simulation, and for predicting the behavior of a system under those uncertainties. It addresses a critical knowledge gap in the widespread adoption of simulation in high-consequence decision-making throughout the engineering and physical sciences. Constructing sophisticated techniques for prediction from basic building blocks, the book first reviews the fundamentals that underpin later topics of the book including probability, sampling, and Bayesian statistics. Part II focuses on applying Local Sensitivity Analysis to apportion uncertainty in the model outputs to sources of uncertainty in its inputs. Part III demonstrates techniques for quantifying the impact of parametric uncertainties on a problem, specifically how input uncertainties affect outputs. The final section covers techniques for applying uncertainty quantification to make predictions under uncertainty, including treatment of epistemic uncertainties. It presents the theory and practice of predicting the behavior of a system based on the aggregation of data from simulation, theory, and experiment. The text focuses on simulations based on the solution of systems of partial differential equations and includes in-depth coverage of Monte Carlo methods, basic design of computer experiments, as well as regularized statistical techniques. Code references, in python, appear throughout the text and online as executable code, enabling readers to perform the analysis under discussion. Worked examples from realistic, model problems help readers understand the mechanics of applying the methods. Each chapter ends with several assignable problems. Uncertainty Quantification and Predictive Computational Science fills the growing need for a classroom text for senior undergraduate and early-career graduate students in the engineering and physical sciences and supports independent study by researchers and professionals who must include uncertainty quantification and predictive science in the simulations they develop and/or perform.

Spectral Methods for Uncertainty Quantification

Author: Olivier Le Maitre

Publisher: Springer Science & Business Media

Published: 2010-03-11

Total Pages: 542

ISBN-13: 9048135206

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This book deals with the application of spectral methods to problems of uncertainty propagation and quanti?cation in model-based computations. It speci?cally focuses on computational and algorithmic features of these methods which are most useful in dealing with models based on partial differential equations, with special att- tion to models arising in simulations of ?uid ?ows. Implementations are illustrated through applications to elementary problems, as well as more elaborate examples selected from the authors’ interests in incompressible vortex-dominated ?ows and compressible ?ows at low Mach numbers. Spectral stochastic methods are probabilistic in nature, and are consequently rooted in the rich mathematical foundation associated with probability and measure spaces. Despite the authors’ fascination with this foundation, the discussion only - ludes to those theoretical aspects needed to set the stage for subsequent applications. The book is authored by practitioners, and is primarily intended for researchers or graduate students in computational mathematics, physics, or ?uid dynamics. The book assumes familiarity with elementary methods for the numerical solution of time-dependent, partial differential equations; prior experience with spectral me- ods is naturally helpful though not essential. Full appreciation of elaborate examples in computational ?uid dynamics (CFD) would require familiarity with key, and in some cases delicate, features of the associated numerical methods. Besides these shortcomings, our aim is to treat algorithmic and computational aspects of spectral stochastic methods with details suf?cient to address and reconstruct all but those highly elaborate examples.

Project Risk Quantification

Author: John K. Hollmann

Publisher:

Published: 2016-06-28

Total Pages: 416

ISBN-13: 9781941075029

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Project Risk Quantification presents the most practical, realistic, and integrated approach to project cost and schedule Risk Quantification that is available today. It offers proven, empirically-valid methods and tools applicable to projects of all types and at all decision gates. The text is written for both the manager and the risk analysis practitioner. It will bring reliable accuracy and contingency determination to your capital project organization.

Introduction to Uncertainty Quantification

Author: T.J. Sullivan

Publisher: Springer

Published: 2015-12-14

Total Pages: 342

ISBN-13: 3319233955

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This text provides a framework in which the main objectives of the field of uncertainty quantification (UQ) are defined and an overview of the range of mathematical methods by which they can be achieved. Complete with exercises throughout, the book will equip readers with both theoretical understanding and practical experience of the key mathematical and algorithmic tools underlying the treatment of uncertainty in modern applied mathematics. Students and readers alike are encouraged to apply the mathematical methods discussed in this book to their own favorite problems to understand their strengths and weaknesses, also making the text suitable for a self-study. Uncertainty quantification is a topic of increasing practical importance at the intersection of applied mathematics, statistics, computation and numerous application areas in science and engineering. This text is designed as an introduction to UQ for senior undergraduate and graduate students with a mathematical or statistical background and also for researchers from the mathematical sciences or from applications areas who are interested in the field. T. J. Sullivan was Warwick Zeeman Lecturer at the Mathematics Institute of the University of Warwick, United Kingdom, from 2012 to 2015. Since 2015, he is Junior Professor of Applied Mathematics at the Free University of Berlin, Germany, with specialism in Uncertainty and Risk Quantification.

Quantification and Syntactic Theory

Author: R. Cooper

Publisher: Springer Science & Business Media

Published: 2013-03-09

Total Pages: 229

ISBN-13: 9401569320

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The format of this book is unusual, especially for a book about linguistics. The book is meant primarily as a research monograph aimed at linguists who have some background in formal semantics, e. g. Montague Grammar. However, I have two other audiences in mind. Linguists who have little or no experience of formal semantics, but who have worked through a basic mathematics for linguists course (e. g. using Wall, 1972, or Partee, 1978), should, perhaps with the help of a sympathetic Montague gramma rian, be able to discover enough of how I have adapted some of the basic ideas in formal semantics to make the developments that I undertake in the rest of the book accessible. Logicians and computer scientists who know about model theoretic semantics and formal systems should be able to glean enough from Chapters I and II about linguistic concerns and techniques to be able to read the remainder of the book, again possibly with the help of a sympathetic Montague grammarian. However, readers should beware. Chapter II is not meant as a general introduction either to formal semantics or to linguistics and while much of the presentation there is going over ground that is already well covered in the literature, the particular formulation and the emphases are very much oriented to the developments to be undertaken later in the book.

Uncertainty Quantification

Author: Christian Soize

Publisher: Springer

Published: 2017-04-24

Total Pages: 329

ISBN-13: 3319543393

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This book presents the fundamental notions and advanced mathematical tools in the stochastic modeling of uncertainties and their quantification for large-scale computational models in sciences and engineering. In particular, it focuses in parametric uncertainties, and non-parametric uncertainties with applications from the structural dynamics and vibroacoustics of complex mechanical systems, from micromechanics and multiscale mechanics of heterogeneous materials. Resulting from a course developed by the author, the book begins with a description of the fundamental mathematical tools of probability and statistics that are directly useful for uncertainty quantification. It proceeds with a well carried out description of some basic and advanced methods for constructing stochastic models of uncertainties, paying particular attention to the problem of calibrating and identifying a stochastic model of uncertainty when experimental data is available. This book is intended to be a graduate-level textbook for students as well as professionals interested in the theory, computation, and applications of risk and prediction in science and engineering fields.

Quantification in the Theory of Grammar

Author: Taisuke Nishigauchi

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 251

ISBN-13: 9400919727

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In the past few decades, the development of theoretical linguistics has proved to be successful in shedding light on the intricate nature of language and knowledge of grammar, which contributes to a deeper understanding of the human mind. This book discusses various issues in syntax and logical structure of natural language from theoretical perspectives. The primary data on which theoretical claims are made is drawn from Japanese and Japanese-type languages, but it also contains discussion of related phenomena in English which have never been discussed from the same viewpoint in the current literature. Although the book is written in the format of a version of the Extended Standard Theory tradition, informally referred to as the Principles and Parameters Approach or 'Government and Binding (OB) Theory', it should be of interest to a much wider audience. The reader interested in other theoretical frameworks will find the discussion in this book easily translatable in the framework of his or her choice - in fact, I would like to claim that the problems posed by this book are inevitable in any theory of syntax and semantics of natural language.