Quantification
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| Author | : Anna Szabolcsi |
| Publisher | : Cambridge University Press |
| Total Pages | : 265 |
| Release | : 2010-01-17 |
| Genre | : Language Arts & Disciplines |
| ISBN | : 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.
| Author | : Sally Engle Merry |
| Publisher | : University of Chicago Press |
| Total Pages | : 260 |
| Release | : 2016-06-10 |
| Genre | : Social Science |
| ISBN | : 022626131X |
We live in a world where seemingly everything can be measured. We rely on indicators to translate social phenomena into simple, quantified terms, which in turn can be used to guide individuals, organizations, and governments in establishing policy. Yet counting things requires finding a way to make them comparable. And in the process of translating the confusion of social life into neat categories, we inevitably strip it of context and meaning—and risk hiding or distorting as much as we reveal. With The Seductions of Quantification, leading legal anthropologist Sally Engle Merry investigates the techniques by which information is gathered and analyzed in the production of global indicators on human rights, gender violence, and sex trafficking. Although such numbers convey an aura of objective truth and scientific validity, Merry argues persuasively that measurement systems constitute a form of power by incorporating theories about social change in their design but rarely explicitly acknowledging them. For instance, the US State Department’s Trafficking in Persons Report, which ranks countries in terms of their compliance with antitrafficking activities, assumes that prosecuting traffickers as criminals is an effective corrective strategy—overlooking cultures where women and children are frequently sold by their own families. As Merry shows, indicators are indeed seductive in their promise of providing concrete knowledge about how the world works, but they are implemented most successfully when paired with context-rich qualitative accounts grounded in local knowledge.
| Author | : J. Samuel Barkin |
| Publisher | : University of Michigan Press |
| Total Pages | : 291 |
| Release | : 2017-01-27 |
| Genre | : Philosophy |
| ISBN | : 0472053396 |
Revolutionary volume demonstrates how crossing the positivist and post-positivist divide improves political science research
| Author | : Steffen Mau |
| Publisher | : John Wiley & Sons |
| Total Pages | : 147 |
| Release | : 2019-02-25 |
| Genre | : Social Science |
| ISBN | : 1509530436 |
In today’s world, numbers are in the ascendancy. Societies dominated by star ratings, scores, likes and lists are rapidly emerging, as data are collected on virtually every aspect of our lives. From annual university rankings, ratings agencies and fitness tracking technologies to our credit score and health status, everything and everybody is measured and evaluated. In this important new book, Steffen Mau offers a critical analysis of this increasingly pervasive phenomenon. While the original intention behind the drive to quantify may have been to build trust and transparency, Mau shows how metrics have in fact become a form of social conditioning. The ubiquitous language of ranking and scoring has changed profoundly our perception of value and status. What is more, through quantification, our capacity for competition and comparison has expanded significantly – we can now measure ourselves against others in practically every area. The rise of quantification has created and strengthened social hierarchies, transforming qualitative differences into quantitative inequalities that play a decisive role in shaping the life chances of individuals. This timely analysis of the pernicious impact of quantification will appeal to students and scholars across the social sciences, as well as anyone concerned by the cult of numbers and its impact on our lives and societies today.
| Author | : Ryan G. McClarren |
| Publisher | : Springer |
| Total Pages | : 349 |
| Release | : 2018-11-23 |
| Genre | : Science |
| ISBN | : 3319995251 |
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.
| Author | : Charles L. Webber, Jr. |
| Publisher | : Springer |
| Total Pages | : 426 |
| Release | : 2014-07-31 |
| Genre | : Science |
| ISBN | : 3319071556 |
The analysis of recurrences in dynamical systems by using recurrence plots and their quantification is still an emerging field. Over the past decades recurrence plots have proven to be valuable data visualization and analysis tools in the theoretical study of complex, time-varying dynamical systems as well as in various applications in biology, neuroscience, kinesiology, psychology, physiology, engineering, physics, geosciences, linguistics, finance, economics, and other disciplines. This multi-authored book intends to comprehensively introduce and showcase recent advances as well as established best practices concerning both theoretical and practical aspects of recurrence plot based analysis. Edited and authored by leading researcher in the field, the various chapters address an interdisciplinary readership, ranging from theoretical physicists to application-oriented scientists in all data-providing disciplines.
| Author | : Olivier Le Maitre |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 542 |
| Release | : 2010-03-11 |
| Genre | : Science |
| ISBN | : 9048135206 |
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.
| Author | : T.J. Sullivan |
| Publisher | : Springer |
| Total Pages | : 351 |
| Release | : 2015-12-14 |
| Genre | : Mathematics |
| ISBN | : 3319233955 |
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.
| Author | : Christian Soize |
| Publisher | : Springer |
| Total Pages | : 344 |
| Release | : 2017-04-24 |
| Genre | : Computers |
| ISBN | : 3319543393 |
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.
| Author | : Ralph C. Smith |
| Publisher | : SIAM |
| Total Pages | : 400 |
| Release | : 2013-12-02 |
| Genre | : Computers |
| ISBN | : 161197321X |
The field of uncertainty quantification is evolving rapidly because of increasing emphasis on models that require quantified uncertainties for large-scale applications, novel algorithm development, and new computational architectures that facilitate implementation of these algorithms. Uncertainty Quantification: Theory, Implementation, and Applications provides readers with the basic concepts, theory, and algorithms necessary to quantify input and response uncertainties for simulation models arising in a broad range of disciplines. The book begins with a detailed discussion of applications where uncertainty quantification is critical for both scientific understanding and policy. It then covers concepts from probability and statistics, parameter selection techniques, frequentist and Bayesian model calibration, propagation of uncertainties, quantification of model discrepancy, surrogate model construction, and local and global sensitivity analysis. The author maintains a complementary web page where readers can find data used in the exercises and other supplementary material.
