Pricemate

Classification of Lipschitz Mappings

Classification of Lipschitz Mappings presents a systematic self-contained treatment of a new classification of Lipschitz mappings and its application in many topics of metric fixed point theory. Suitable for readers interested in metric fixed point theory differential equations and dynamical systems the book only requires a basic background in functional analysis and topology. The author focuses on a more precise classification of Lipschitzian mappings. The mean Lipschitz condition introduced by Goebel Japón Pineda and Sims is relatively easy to check and turns out to satisfy several principles: Regulating the possible growth of the sequence of Lipschitz constants k(Tn)Ensuring good estimates for k0(T) and k∞(T)Providing some new results in metric fixed point theory

GBP 59.99

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The Sharpe Ratio Statistics and Applications

The Sharpe ratio is the most widely used metric for comparing theperformance of financial assets. The Markowitz portfolio is the portfolio withthe highest Sharpe ratio. The Sharpe Ratio: Statistics and Applications examines the statistical propertiesof the Sharpe ratio and Markowitz portfolio both under the simplifying assumption of Gaussian returns and asymptotically. Connections are drawn between the financial measures and classical statistics includingStudent's t Hotelling's T^2 and the Hotelling-Lawley trace. The robustness of these statistics to heteroskedasticity autocorrelation fat tails and skew of returns are considered. The construction of portfolios to maximizethe Sharpe is expanded from the usual static unconditional model to include subspace constraints heding out assets and the use of conditioning information on both expected returns and risk. {book title} is the most comprehensivetreatment of the statistical properties of the Sharpe ratio and Markowitzportfolio ever published. Features: * Material on single asset problems market timing unconditional and conditional portfolio problems hedged portfolios. * Inference via both Frequentist and Bayesian paradigms. *A comprehensive treatment of overoptimism and overfitting of trading strategies. *Advice on backtesting strategies. *Dozens of examples and hundreds of exercises for self study. This book is an essential reference for the practicing quant strategist and the researcher alike and an invaluable textbook for the student. Steven E. Pav holds a PhD in mathematics from Carnegie Mellon University and degrees in mathematics and ceramic engineering sciencefrom Indiana University Bloomington and Alfred University. He was formerly a quantitative strategist at Convexus Advisors and CerebellumCapital and a quantitative analyst at Bank of America. He is the author of a dozen R packages including those for analyzing the significance of the Sharpe ratio and Markowitz portfolio. He writes about the Sharpe ratio at https://protect-us. mimecast. com/s/BUveCPNMYvt0vnwX8Cj689u?domain=sharperat. io . | The Sharpe Ratio Statistics and Applications

GBP 44.99

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Geometric Modeling and Mesh Generation from Scanned Images

Cutting-Edge Techniques to Better Analyze and Predict Complex Physical PhenomenaGeometric Modeling and Mesh Generation from Scanned Images shows how to integrate image processing geometric modeling and mesh generation with the finite element method (FEM) to solve problems in computational biology medicine materials science and engineering. Based on the author’s recent research and course at Carnegie Mellon University the text explains the fundamentals of medical imaging image processing computational geometry mesh generation visualization and finite element analysis. It also explores novel and advanced applications in computational biology medicine materials science and other engineering areas. One of the first to cover this emerging interdisciplinary field the book addresses biomedical/material imaging image processing geometric modeling and visualization FEM and biomedical and engineering applications. It introduces image-mesh-simulation pipelines reviews numerical methods used in various modules of the pipelines and discusses several scanning techniques including ones to probe polycrystalline materials. The book next presents the fundamentals of geometric modeling and computer graphics geometric objects and transformations and curves and surfaces as well as two isocontouring methods: marching cubes and dual contouring. It then describes various triangular/tetrahedral and quadrilateral/hexahedral mesh generation techniques. The book also discusses volumetric T-spline modeling for isogeometric analysis (IGA) and introduces some new developments of FEM in recent years with applications.

GBP 46.99

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Computational Aspects of Polynomial Identities Volume l Kemer's Theorems 2nd Edition

Computational Aspects of Polynomial Identities: Volume l Kemer’s Theorems 2nd Edition presents the underlying ideas in recent polynomial identity (PI)-theory and demonstrates the validity of the proofs of PI-theorems. This edition gives all the details involved in Kemer’s proof of Specht’s conjecture for affine PI-algebras in characteristic 0. The book first discusses the theory needed for Kemer’s proof including the featured role of Grassmann algebra and the translation to superalgebras. The authors develop Kemer polynomials for arbitrary varieties as tools for proving diverse theorems. They also lay the groundwork for analogous theorems that have recently been proved for Lie algebras and alternative algebras. They then describe counterexamples to Specht’s conjecture in characteristic p as well as the underlying theory. The book also covers Noetherian PI-algebras Poincaré–Hilbert series Gelfand–Kirillov dimension the combinatoric theory of affine PI-algebras and homogeneous identities in terms of the representation theory of the general linear group GL. Through the theory of Kemer polynomials this edition shows that the techniques of finite dimensional algebras are available for all affine PI-algebras. It also emphasizes the Grassmann algebra as a recurring theme including in Rosset’s proof of the Amitsur–Levitzki theorem a simple example of a finitely based T-ideal the link between algebras and superalgebras and a test algebra for counterexamples in characteristic p. | Computational Aspects of Polynomial Identities Volume l Kemer's Theorems 2nd Edition

GBP 59.99

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A Course in Categorical Data Analysis

Categorical data-comprising counts of individuals objects or entities in different categories-emerge frequently from many areas of study including medicine sociology geology and education. They provide important statistical information that can lead to real-life conclusions and the discovery of fresh knowledge. Therefore the ability to manipulate understand and interpret categorical data becomes of interest-if not essential-to professionals and students in a broad range of disciplines. Although t-tests linear regression and analysis of variance are useful valid methods for analysis of measurement data categorical data requires a different methodology and techniques typically not encountered in introductory statistics courses. Developed from long experience in teaching categorical analysis to a multidisciplinary mix of undergraduate and graduate students A Course in Categorical Data Analysis presents the easiest most straightforward ways of extracting real-life conclusions from contingency tables. The author uses a Fisherian approach to categorical data analysis and incorporates numerous examples and real data sets. Although he offers S-PLUS routines through the Internet readers do not need full knowledge of a statistical software package. In this unique text the author chooses methods and an approach that nurtures intuitive thinking. He trains his readers to focus not on finding a model that fits the data but on using different models that may lead to meaningful conclusions. The book offers some simple innovative techniques not highighted in other texts that help make the book accessible to a broad interdisciplinary audience. A Course in Categorical Data Analysis enables readers to quickly use its offering of tools for drawing scientific medical or real-life conclusions from categorical data sets.

GBP 170.00

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Introduction to Real Analysis

This classic textbook has been used successfully by instructors and students for nearly three decades. This timely new edition offers minimal yet notable changes while retaining all the elements presentation and accessible exposition of previous editions. A list of updates is found in the Preface to this edition. This text is based on the author’s experience in teaching graduate courses and the minimal requirements for successful graduate study. The text is understandable to the typical student enrolled in the course taking into consideration the variations in abilities background and motivation. Chapters one through six have been written to be accessible to the average student w hile at the same time challenging the more talented student through the exercises. Chapters seven through ten assume the students have achieved some level of expertise in the subject. In these chapters the theorems examples and exercises require greater sophistication and mathematical maturity for full understanding. In addition to the standard topics the text includes topics that are not always included in comparable texts. Chapter 6 contains a section on the Riemann-Stieltjes integral and a proof of Lebesgue’s t heorem providing necessary and sufficient conditions for Riemann integrability. Chapter 7 also includes a section on square summable sequences and a brief introduction to normed linear spaces. C hapter 8 contains a proof of the Weierstrass approximation theorem using the method of aapproximate identities. The inclusion of Fourier series in the text allows the student to gain some exposure to this important subject. The final chapter includes a detailed treatment of Lebesgue measure and the Lebesgue integral using inner and outer measure. The exercises at the end of each section reinforce the concepts. Notes provide historical comments or discuss additional topics. | Introduction to Real Analysis

GBP 46.99

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Risks of Artificial Intelligence

If the intelligence of artificial systems were to surpass that of humans humanity would face significant risks. The time has come to consider these issues and this consideration must include progress in artificial intelligence (AI) as much as insights from AI theory. Featuring contributions from leading experts and thinkers in artificial intelligence Risks of Artificial Intelligence is the first volume of collected chapters dedicated to examining the risks of AI. The book evaluates predictions of the future of AI proposes ways to ensure that AI systems will be beneficial to humans and then critically evaluates such proposals. The book covers the latest research on the risks and future impacts of AI. It starts with an introduction to the problem of risk and the future of artificial intelligence followed by a discussion (Armstrong/Sokala/ÓhÉigeartaigh) on how predictions of its future have fared to date. Omohundro makes the point that even an innocuous artificial agent can easily turn into a serious threat for humans. T. Goertzel explains how to succeed in the design of artificial agents. But will these be a threat for humanity or a useful tool? Ways to assure beneficial outcomes through ‘machine ethics’ and ‘utility functions’ are discussed by Brundage and Yampolskiy. B. Goertzel and Potapov/Rodionov propose ‘learning’ and ‘empathy’ as paths towards safer AI while Kornai explains how the impact of AI may be bounded. Sandberg explains the implications of human-like AI via the technique of brain emulation. Dewey discusses strategies to deal with the ‘fast takeoff’ of artificial intelligence and finally Bishop explains why there is no need to worry because computers will remain in a state of ‘artificial stupidity’. Sharing insights from leading thinkers in artificial intelligence this book provides you with an expert-level perspective of what is on the horizon for AI whether it will be a threat for humanity and how we might counteract this threat.

GBP 44.99

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