Libros de Matemáticas, página 6 de 470

Decision Making Models A Perspective of Fuzzy Logic and Machine Learning presents the latest developments in the field of uncertain mathematics and decision science The book aims to deliver a systematic exposure to soft computing techniques in fuzzy mathematics as well as artificial intelligence in the context of real life problems and is designed to address recent techniques to solving uncertain problems encountered specifically in decision sciences Researchers professors software engineers and graduate students working in the fields of applied mathematics software engineering and artificial intelligence will find this book useful to acquire a solid foundation in fuzzy logic and fuzzy systems Other areas of note include optimization problems and artificial intelligence practices as well as how to analyze IoT solutions with applications and develop decision making mechanisms realized under uncertainty

In the theory of minimal submanifolds, Bernstein''s problem and Plateau''s problem are central topics. This important book presents the DouglasRado solution to Plateau''s problem, but the main emphasis is on Bernstein''s problem and its new developments in various directions: the value distribution of the Gauss image of a minimal surface in Euclidean 3-space, Simons'' work for minimal graphic hypersurfaces, and the author''s own contributions to Bernstein type theorems for higher codimension. The author also introduces some related topics, such as submanifolds with parallel mean curvature, Weierstrass type representation for surfaces of mean curvature 1 in hyperbolic 3-space, and special Lagrangian submanifolds.This new edition contains the author''s recent work on the LawsonOsserman''s problem for higher codimension, and on Chern''s problem for minimal hypersurfaces in the sphere. Both Chern''s problem and LawsonOsserman''s problem are important problems in minimal surface theory which are still unsolved. In addition, some new techniques were developed to address those problems in detail, which are of interest in the field of geometric analysis.Readership: Researchers and graduate students in differential geometry.

It has been over a decade since the release first edition of the now classic original edition of Murray''s Mathematical Biology.Since then mathematical biology and medicine has grown at an astonishing rate and has established itself as a distinct discipline. Mathematical modelling is now being applied in every major discipline in the biomedical sciences. Though the field has become increasingly large and specialized, this book remains important as a text that introduces some of the exciting problems which arise in the biomedical sciences and gives some indication of the wide spectrum of questions that modelling can address.Due to the tremendous development in recent years, this new edition is being published in two volumes. This second volume covers spatial models and biomedical applications. For this new edition, Murray covers certain items in depth, introducing new applications such as modelling growth and control of brain tumours, bacterial patterns, wound healing and wolf territoriality. In other areas, he discusses basic modelling concepts and provides further references as needed. He also provides even closer links between models and experimental data throughout the text.Graduate students and researchers will find this book invaluable as it gives an excellent background from which to begin genuinely practical interdisciplinary research in the biomedical sciences.

Understanding approximate dynamic programming (ADP) in large industrial settings helps develop practical and high-quality solutions to problems that involve making decisions in the presence of uncertainty. With a focus on modeling and algorithms in conjunction with the language of mainstream operations research, artificial intelligence, and control theory, this second edition of Approximate Dynamic Programming Solving the Curses of Dimensionality uniquely integrates four distinct disciplines?Markov design processes, mathematical programming, simulation, and statistics?to show students, practitioners, and researchers how to successfully model and solve a wide range of real-life problems using ADP.

This book is divided into fourteen chapters, with 18 appendices as introduction to prerequisite topological and algebraic knowledge, etc. The first seven chapters focus on local analysis. This part can be used as a fundamental textbook for graduate students of theoretical physics. Chapters 810 discuss geometry on fibre bundles, which facilitates further reference for researchers. The last four chapters deal with the Atiyah-Singer index theorem, its generalization and its application, quantum anomaly, cohomology field theory and noncommutative geometry, giving the reader a glimpse of the frontier of current research in theoretical physics.- Differentiable Manifolds and Differential Forms- Transformation of Manifold, Manifolds with Given Vector Fields and Lie Group Manifold- Affine Connection and Covariant Differentiation- Riemannian Manifold- Symplectic Manifold and Contact Manifold- Complex Manifolds- Homology of Manifolds- Homotopy of Manifold, Fibre Bundle, Classification of Fibre Bundles- Differential Geometry of Fibre Bundle, YangMills Gauge Theory- Characteristic Classes- The AtiyahSinger Index Theorem- Index Theorem on Manifold with Boundary and on Open Infinite Manifold- Family Index Theorem, Topological Properties of Quantum Gauge Theory- Noncommutative Geometry, Quantum Group, and q-Deformation of Chern-Characters

Modern apparatuses allow us to collect samples of functional data, mainly curves but also images. On the other hand, nonparametric statistics produces useful tools for standard data exploration. This book links these two fields of modern statistics by explaining how functional data can be studied through parameter-free statistical ideas. At the same time it shows how functional data can be studied through parameter-free statistical ideas, and offers an original presentation of new nonparametric statistical methods for functional data analysis.

Praise for the Fourth Edition "As with previous editions, the authors have produced a leading textbook on regression." Journal of the American Statistical Association A comprehensive and up-to-date introduction to the fundamentals of regression analysis Introduction to Linear Regression Analysis, Fifth Edition continues to present both the conventional and less common uses of linear regression in today s cutting-edge scientific research. The authors blend both theory and application to equip readers with an understanding of the basic principles needed to apply regression model-building techniques in various fields of study, including engineering, management, and the health sciences. Following a general introduction to regression modeling, including typical applications, a host of technical tools are outlined such as basic inference procedures, introductory aspects of model adequacy checking, and polynomial regression models and their variations. The book then discusses how transformations and weighted least squares can be used to resolve problems of model inadequacy and also how to deal with influential observations. The Fifth Edition features numerous newly added topics, including: A chapter on regression analysis of time series data that presents the Durbin-Watson test and other techniques for detecting autocorrelation as well as parameter estimation in time series regression models Regression models with random effects in addition to a discussion on subsampling and the importance of the mixed model Tests on individual regression coefficients and subsets of coefficients Examples of current uses of simple linear regression models and the use of multiple regression models for understanding patient satisfaction data. In addition to Minitab, SAS, and S-PLUS, the authors have incorporated JMP and the freely available R software to illustrate the discussed techniques and procedures in this new edition. Numerous exercises have been added throughout, allowing readers to test their understanding of the material. Introduction to Linear Regression Analysis, Fifth Edition is an excellent book for statistics and engineering courses on regression at the upper-undergraduate and graduate levels. The book also serves as a valuable, robust resource for professionals in the fields of engineering, life and biological sciences, and the social sciences.

Ostebee and Zorn'' s approach applies reform principles to a rigorous calculus text. Conceptual understanding is the main goal of the text, and looking at mathematics from many representations (graphical, symbolic, numerical) is the main strategy for achieving this type of understanding. The key strengths of the text include combining symbolic manipulation with graphical and numerical representation, exercises of a varied nature and difficulty, and explanations written to be understandable to student readers.A student-friendly and approachable tone, numerous examples, critical-thinking questions, and supportive details and commentary help students successfully read and use the text.Representation of mathematical concepts through a variety of viewpoints supports different learning styles.Proofs presented at point of use contribute significantly to helping students understand rigorous calculus concepts and develop analytic skills."Further Exercises are a little more ambitious and may requirethe synthesis of several ideas, a deeper or more sophisticated understanding of basic concepts, or the use of a computer algebra system such as ...

How do you bring real world scenarios into your course? Or would you like to? Business Snapshots (about 60 in total, found in almost all chapters) - Carefully thought out and integrated into the main material in chapters. *Describe real world situations and interesting issues that are highlighted to illustrate points being made throughout the text. Completely revised chapters on credit risk and credit derivatives (Ch 20 and 21) - Reflects market developments. *Makes these chapters in this edition more straightforward and easier to teach. Opening six chapters have been replaced by seven chapters that cover forward, futures, and swaps in a more student-friendly way. *The main change here is that the chapter on interest rates has been split into two chapters, one devoted to the math involving interest rates, the other to interest rate futures contracts and how they are used in hedging, which will be easier for students to grasp. More discussion of how models can be implemented in Excel - Monte Carlo simulation in Chapter 17, GARCH models in Chapter 19, and the variance-gamma model in Chapter 24.*Includes examples in the book and Excel spreadsheets on Web site to illustrate applications. A series of Technical Notes - Available on the author's Web site to accompany the book. *Creates a streamlined and more student friendly presentation by including less purely technical material in the book. Separate chapter on Convexity, Timing, and Quanto adjustments. *Affords succinct and targeted coverage of these concepts for deeper coverage students need. New topics *For example, the size of derivatives markets is discussed in Chapter 1, Basel II is discussed in Chapter 20, and the variance-gamma model is covered in Chapter 24. More in-depth coverage *For example, convexity adjustments to Eurodollar futures in Chapter 5, copula models in Chapters 20 and 21, and executive stock options in Chapters 8 and 13

Springer-Verlag New York Inc., United States, 2010. Paperback. Book Condition: New. Language: English . Brand New Book ***** Print on Demand *****.This volume, which is completely dedicated to continuous bivariate dist- butions, describes in detail their forms, properties, dependence structures, computation, and applications. It is a comprehensive and thorough revision ofanearliereditionof ContinuousBivariateDistributions,Emphasizing- plications by T.P. Hutchinson and C.D. Lai, published in 1990 by Rumsby Scienti?c Publishing, Adelaide, Australia. It has been nearly two decades since the publication of that book, and much has changed in this area of research during this period. Generali- tions have been considered for many known standard bivariate distributions. Skewed versions of di?erent bivariate distributions have been proposed and appliedtomodeldatawithskewnessdepartures.Byspecifyingthetwocon- tional distributions, rather than the simple speci?cation of one marginal and one conditional distribution, several general families of conditionally spe- ?ed bivariate distributions have been derived and studied at great length. Finally, bivariate distributions generated by a variety of copulas and their ?exibility (in terms of accommodating association/correlation) and str- tural properties have received considerable attention. All these developments andadvancesnecessitatedthepresentvolumeandhavethusresultedinas- stantially di?erent version than the last edition, both in terms of coverage and topics of discussion. Softcover reprint of hardcover 2nd ed. 2009. Bookseller Inventory # AAV9781441918758

The second edition of this textbook provides a fully updated approach to fuzzy sets and systems that can model uncertainty i e type 2 fuzzy sets and systems The author demonstrates how to overcome the limitations of classical fuzzy sets and systems enabling a wide range of applications from time series forecasting to knowledge mining to control In this new edition a bottom up approach is presented that begins by introducing classical type 1 fuzzy sets and systems and then explains how they can be modified to handle uncertainty The author covers fuzzy rule based systems from type 1 to interval type 2 to general type 2 in one volume For hands on experience the book provides information on accessing MatLab and Java software to complement the content The book features a full suite of classroom material