Libros de Estudios generales, página 3 de 183

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

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.

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

Decision makers in many areas, from industry to engineering and the social sector, face an increasing need to consider multiple, conflicting objectives in their decision processes.In many cases these real world decision problems can be formulated as multicriteria mathematical optimization models. The solution of such models requires appropriate techniques to compute so called efficient, or Pareto optimal, or compromise solutions that - unlike traditional mathematical programming methods - take the contradictory nature of the criteria into account. This book provides the necessary mathematical foundation of multicriteria optimization to solve nonlinear, linear and combinatorial problems with multiple criteria. Motivational examples illustrate the use of multicriteria optimization in practice. Numerous illustrations and exercises as well as an extensive bibliography are provided.In the new edition a chapter on optimality conditions has been added. The linear programming part has been extended and includes new developments. Moreover, motivational examples are now introducing the majority of chapters.

Targeted to both high level under graduate and first level graduate students in mathematics, physics and engineering. Offers a unitary and modern presentation of the main concepts and results. Simple introduction to several advanced topics.Plentiful illuminating examples and applications. Contains 36 figures and 133 completely solved exercises and problems. Written in an accessible and refreshing style.

Symmetry has always played an important role in mechanics, from fundamental formulations of basic principles to concrete applications. The theme of the book is to develop the basic theory and applications of mechanics with an emphasis on the role of symmetry. In recent times, the interest in mechanics, and in symmetry techniques in particular, has accelerated because of developments in dynamical systems, the use of geometric methods and new applications to integrable and chaotic systems, control systems, stability and bifurcation, and the study of specific rigid, fluid, plasma and elastic systems. "Introduction to Mechanics and Symmetry" lays the basic foundation for these topics and includes numerous specific applications, making it beneficial to physicists and engineers. This text has specific examples and applications showing how the theory works, and up-to-date techniques, all of which makes it accessible to a wide variety of readers, especially senior undergraduate and graduate students in mathematics, physics and engineering. For this second edition, the text has been rewritten and updated for clarity throughout, with a major revamping and expansion of the exercises.Internet supplements containing additional material are also available on-line.

In the quarter of a century since three mathematicians and game theorists collaborated to create Winning Ways for Your Mathematical Plays, the book has become the definitive work on the subject of mathematical games. Now carefully revised and broken down into four volumes to accommodate new developments, the second edition retains the original''s wealth of wit and wisdom. The authors'' insightful strategies, blended with their witty and irreverent style, make reading a profitable pleasure. In Volume 3, the authors examine Games played in Clubs, giving case studies for coing and paper-and-pencil games, such as Dots-and-Boxes and Nimstring.

Learn How to Use Growth Curve Analysis with Your Time Course Data An increasingly prominent statistical tool in the behavioral sciences, multilevel regression offers a statistical framework for analyzing longitudinal or time course data. It also provides a way to quantify and analyze individual differences, such as developmental and neuropsychological, in the context of a model of the overall group effects. To harness the practical aspects of this useful tool, behavioral science researchers need a concise, accessible resource that explains how to implement these analysis methods. Growth Curve Analysis and Visualization Using R provides a practical, easy-to-understand guide to carrying out multilevel regression/growth curve analysis (GCA) of time course or longitudinal data in the behavioral sciences, particularly cognitive science, cognitive neuroscience, and psychology. With a minimum of statistical theory and technical jargon, the author focuses on the concrete issue of applying GCA to behavioral science data and individual differences. The book begins with discussing problems encountered when analyzing time course data, how to visualize time course data using the ggplot2 package, and how to format data for GCA and plotting. It then presents a conceptual overview of GCA and the core analysis syntax using the lme4 package and demonstrates how to plot model fits. The book describes how to deal with change over time that is not linear, how to structure random effects, how GCA and regression use categorical predictors, and how to conduct multiple simultaneous comparisons among different levels of a factor. It also compares the advantages and disadvantages of approaches to implementing logistic and quasi-logistic GCA and discusses how to use GCA to analyze individual differences as both fixed and random effects. The final chapter presents the code for all of the key examples along with samples demonstrating how to report GCA results. Throughout the book, R code illustrates how to implement the analyses and generate the graphs. Each chapter ends with exercises to test your understanding. The example datasets, code for solutions to the exercises, and supplemental code and examples are available on the author's website.