Students of mathematical biology discover modern methods of taxonomy with this text, which introduces taxonomic characters, the measurement of similarity, and the analysis of principal components. Other topics include multidimensional scaling, cluster analysis, identification and assignment techniques, more. A familiarity with matrix algebra and elementary statistics are the sole prerequisites.
Providing an introduction to mathematical analysis as it applies to economic theory and econometrics, this book bridges the gap that has separated the teaching of basic mathematics for economics and the increasingly advanced mathematics demanded in economics research today. Dean Corbae, Maxwell B. Stinchcombe, and Juraj Zeman equip students with the knowledge of real and functional analysis and measure theory they need to read and do research in economic and econometric theory. Unlike other mathematics textbooks for economics, An Introduction to Mathematical Analysis for Economic Theory and Econometrics takes a unified approach to understanding basic and advanced spaces through the application of the Metric Completion Theorem. This is the concept by which, for example, the real numbers complete the rational numbers and measure spaces complete fields of measurable sets. Another of the book's unique features is its concentration on the mathematical foundations of econometrics. To illustrate difficult concepts, the authors use simple examples drawn from economic theory and econometrics. Accessible and rigorous, the book is self-contained, providing proofs of theorems and assuming only an undergraduate background in calculus and linear algebra. Begins with mathematical analysis and economic examples accessible to advanced undergraduates in order to build intuition for more complex analysis used by graduate students and researchers Takes a unified approach to understanding basic and advanced spaces of numbers through application of the Metric Completion Theorem Focuses on examples from econometrics to explain topics in measure theory
Taxonomy comprises a broad variety of activities related to the construction of classificatory systems. Over the past several years, the development of numerical and mathematical techniques designed to produce more objective results has transformed the field. This text offers students of mathematical biology an introduction to modern methods of taxonomy. Starting with an introduction to the philosophy and aims of numerical taxonomy, the text considers taxonomic characters and the measurement of similarity. An analysis of principal components presents geometric and mathematical interpretations; other chapters explore multidimensional scaling, cluster analysis, identification and assignment techniques, and the construction of evolutionary trees. Each of the eight major sections concludes with a helpful summary of its contents. In addition to its value to undergraduates, this text should also prove practical for postgraduate students and researchers interested in taxonomy and in the use of numerical methods in evolutionary studies. A familiarity with matrix algebra and elementary statistics are the sole prerequisites. Book jacket.
Since the publication of the first edition of this book, advances in algorithms, logic and software tools have transformed the field of data fusion. The latest edition covers these areas as well as smart agents, human computer interaction, cognitive aides to analysis and data system fusion control. data fusion system, this book guides you through the process of determining the trade-offs among competing data fusion algorithms, selecting commercial off-the-shelf (COTS) tools, and understanding when data fusion improves systems processing. Completely new chapters in this second edition explain data fusion system control, DARPA's recently developed TRIP model, and the latest applications of data fusion in data warehousing and medical equipment, as well as defence systems.
Employing a practical, "learn by doing" approach, this first-rate text fosters the development of the skills beyond the pure mathematics needed to set up and manipulate mathematical models. The author draws on a diversity of fields — including science, engineering, and operations research — to provide over 100 reality-based examples. Students learn from the examples by applying mathematical methods to formulate, analyze, and criticize models. Extensive documentation, consisting of over 150 references, supplements the models, encouraging further research on models of particular interest. The lively and accessible text requires only minimal scientific background. Designed for senior college or beginning graduate-level students, it assumes only elementary calculus and basic probability theory for the first part, and ordinary differential equations and continuous probability for the second section. All problems require students to study and create models, encouraging their active participation rather than a mechanical approach. Beyond the classroom, this volume will prove interesting and rewarding to anyone concerned with the development of mathematical models or the application of modeling to problem solving in a wide array of applications.
In the last few years, there has been an enormous amount of activity in the study of analogy and metaphor. This is partly because of an interest of artificial intelligence researchers in simulating learning processes using analogy. It also arises from critical examinations of standard theories in the philosophy of language, with their inbuilt literal/meta phoric distinction. This volume consists of recent previously unpub lished work in this area, with a particular emphasis upon the role of analogies in reasoning and, more generally, their role in thought and language. The papers are contributed by philosophers, computer scientists, cognitive scientists and literary critics. Researchers in these fields whose focus is the study of analogy and metaphor will find much of interest in this volume. These essays can also serve as an introduction to some of the major approaches taken in the investigation of analogy. As noted, this volume brings together the work of researchers in several different disciplines. The various approaches taken with respect to the understanding of analogy tend to be rather different, however, the articles suggest a common conclusion. Analogy and metaphor pervade thought and language; their close investigation thus constitutes a valuable contribution to our understanding of persons. DAVID H. HELMAN Case Western Reserve University vii PART I CONCEPTUAL AND CATEGORICAL THEORIES OF ANALOGICAL UNDERSTANDING MARK TURNER CATEGORIES AND ANALOGIES I want to pursue the following claims: The way we categorize helps explain the way we recognize a statement as an analogy.
Classic of modern biology sets forth seminal "theory of transformation" ? that evolution takes place in large-scale transformations of body as a whole. Over 500 photographs and drawings.
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How do we group different subjects on a variety of variables? Should we use a classification procedure in which only the concepts are classified (typology), one in which only empirical entities are classified (taxonomy), or some combination of both? In this clearly written book, Bailey addresses these questions and shows how classification methods can be used to improve research. Beginning with an exploration of the advantages and disadvantages of classification procedures including those typologies that can be constructed without the use of a computer, the book covers such topics as clustering procedures (including agglomerative and divisive methods), the relationship among various classification techniques (including the relationship of monothetic, qualitative typologies to polythetic, quantitative taxonomies), a comparison of clustering methods and how these methods compare with related statistical techniques such as factor analysis, multidimensional scaling and systems analysis, and lists classification resources. This volume also discusses software packages for use in clustering techniques.
