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Mathematical Foundations For Social Analysis
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Book Synopsis Mathematical Foundations of Time Series Analysis by : Jan Beran
Download or read book Mathematical Foundations of Time Series Analysis written by Jan Beran and published by Springer. This book was released on 2018-03-23 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a concise introduction to the mathematical foundations of time series analysis, with an emphasis on mathematical clarity. The text is reduced to the essential logical core, mostly using the symbolic language of mathematics, thus enabling readers to very quickly grasp the essential reasoning behind time series analysis. It appeals to anybody wanting to understand time series in a precise, mathematical manner. It is suitable for graduate courses in time series analysis but is equally useful as a reference work for students and researchers alike.
Book Synopsis Mathematical Foundations for Data Analysis by : Jeff M. Phillips
Download or read book Mathematical Foundations for Data Analysis written by Jeff M. Phillips and published by Springer Nature. This book was released on 2021-03-29 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook, suitable for an early undergraduate up to a graduate course, provides an overview of many basic principles and techniques needed for modern data analysis. In particular, this book was designed and written as preparation for students planning to take rigorous Machine Learning and Data Mining courses. It introduces key conceptual tools necessary for data analysis, including concentration of measure and PAC bounds, cross validation, gradient descent, and principal component analysis. It also surveys basic techniques in supervised (regression and classification) and unsupervised learning (dimensionality reduction and clustering) through an accessible, simplified presentation. Students are recommended to have some background in calculus, probability, and linear algebra. Some familiarity with programming and algorithms is useful to understand advanced topics on computational techniques.
Book Synopsis General Systems Theory: Mathematical Foundations by :
Download or read book General Systems Theory: Mathematical Foundations written by and published by Academic Press. This book was released on 1975-03-21 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation;methods for low-rank matrix approximations; hybrid methods based on a combination of iterative procedures and best operator approximation; andmethods for information compression and filtering under condition that a filter model should satisfy restrictions associated with causality and different types of memory.As a result, the book represents a blend of new methods in general computational analysis,and specific, but also generic, techniques for study of systems theory ant its particularbranches, such as optimal filtering and information compression.- Best operator approximation,- Non-Lagrange interpolation,- Generic Karhunen-Loeve transform- Generalised low-rank matrix approximation- Optimal data compression- Optimal nonlinear filtering
Book Synopsis Foundations of Mathematical Analysis by : Saminathan Ponnusamy
Download or read book Foundations of Mathematical Analysis written by Saminathan Ponnusamy and published by Springer Science & Business Media. This book was released on 2011-12-16 with total page 575 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical analysis is fundamental to the undergraduate curriculum not only because it is the stepping stone for the study of advanced analysis, but also because of its applications to other branches of mathematics, physics, and engineering at both the undergraduate and graduate levels. This self-contained textbook consists of eleven chapters, which are further divided into sections and subsections. Each section includes a careful selection of special topics covered that will serve to illustrate the scope and power of various methods in real analysis. The exposition is developed with thorough explanations, motivating examples, exercises, and illustrations conveying geometric intuition in a pleasant and informal style to help readers grasp difficult concepts. Foundations of Mathematical Analysis is intended for undergraduate students and beginning graduate students interested in a fundamental introduction to the subject. It may be used in the classroom or as a self-study guide without any required prerequisites.
Book Synopsis Mathematical Foundations of Data Science Using R by : Frank Emmert-Streib
Download or read book Mathematical Foundations of Data Science Using R written by Frank Emmert-Streib and published by Walter de Gruyter GmbH & Co KG. This book was released on 2022-10-24 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of the book is to help students become data scientists. Since this requires a series of courses over a considerable period of time, the book intends to accompany students from the beginning to an advanced understanding of the knowledge and skills that define a modern data scientist. The book presents a comprehensive overview of the mathematical foundations of the programming language R and of its applications to data science.
Book Synopsis Mathematical Foundations of Game Theory by : Rida Laraki
Download or read book Mathematical Foundations of Game Theory written by Rida Laraki and published by Springer Nature. This book was released on 2019-09-07 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gives a concise presentation of the mathematical foundations of Game Theory, with an emphasis on strategic analysis linked to information and dynamics. It is largely self-contained, with all of the key tools and concepts defined in the text. Combining the basics of Game Theory, such as value existence theorems in zero-sum games and equilibrium existence theorems for non-zero-sum games, with a selection of important and more recent topics such as the equilibrium manifold and learning dynamics, the book quickly takes the reader close to the state of the art. Applications to economics, biology, and learning are included, and the exercises, which often contain noteworthy results, provide an important complement to the text. Based on lectures given in Paris over several years, this textbook will be useful for rigorous, up-to-date courses on the subject. Apart from an interest in strategic thinking and a taste for mathematical formalism, the only prerequisite for reading the book is a solid knowledge of mathematics at the undergraduate level, including basic analysis, linear algebra, and probability.
Book Synopsis Formal Concept Analysis by : Bernhard Ganter
Download or read book Formal Concept Analysis written by Bernhard Ganter and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: This first textbook on formal concept analysis gives a systematic presentation of the mathematical foundations and their relations to applications in computer science, especially in data analysis and knowledge processing. Above all, it presents graphical methods for representing conceptual systems that have proved themselves in communicating knowledge. The mathematical foundations are treated thoroughly and are illuminated by means of numerous examples, making the basic theory readily accessible in compact form.
Book Synopsis Mathematical Foundations of Big Data Analytics by : Vladimir Shikhman
Download or read book Mathematical Foundations of Big Data Analytics written by Vladimir Shikhman and published by Springer Nature. This book was released on 2021-02-11 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this textbook, basic mathematical models used in Big Data Analytics are presented and application-oriented references to relevant practical issues are made. Necessary mathematical tools are examined and applied to current problems of data analysis, such as brand loyalty, portfolio selection, credit investigation, quality control, product clustering, asset pricing etc. – mainly in an economic context. In addition, we discuss interdisciplinary applications to biology, linguistics, sociology, electrical engineering, computer science and artificial intelligence. For the models, we make use of a wide range of mathematics – from basic disciplines of numerical linear algebra, statistics and optimization to more specialized game, graph and even complexity theories. By doing so, we cover all relevant techniques commonly used in Big Data Analytics.Each chapter starts with a concrete practical problem whose primary aim is to motivate the study of a particular Big Data Analytics technique. Next, mathematical results follow – including important definitions, auxiliary statements and conclusions arising. Case-studies help to deepen the acquired knowledge by applying it in an interdisciplinary context. Exercises serve to improve understanding of the underlying theory. Complete solutions for exercises can be consulted by the interested reader at the end of the textbook; for some which have to be solved numerically, we provide descriptions of algorithms in Python code as supplementary material.This textbook has been recommended and developed for university courses in Germany, Austria and Switzerland.
Book Synopsis Foundations of Mathematical Economics by : Michael Carter
Download or read book Foundations of Mathematical Economics written by Michael Carter and published by MIT Press. This book was released on 2001-10-26 with total page 678 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive introduction to the mathematical foundations of economics, from basic set theory to fixed point theorems and constrained optimization. Rather than simply offer a collection of problem-solving techniques, the book emphasizes the unifying mathematical principles that underlie economics. Features include an extended presentation of separation theorems and their applications, an account of constraint qualification in constrained optimization, and an introduction to monotone comparative statics. These topics are developed by way of more than 800 exercises. The book is designed to be used as a graduate text, a resource for self-study, and a reference for the professional economist.
Book Synopsis Mathematical Foundations for Computing by : G. P. McKeown
Download or read book Mathematical Foundations for Computing written by G. P. McKeown and published by Palgrave. This book was released on 1995 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text gives a description of the fundamental mathematical concepts used by computer scientists, while also emphasizing the need for careful justification. It provides proofs of all the major results, and all the algorithms presented are developed carefully and their performance analyzed. Throughout, the aim is to provide a well-balanced treatment of both the discrete and continuous mathematics that should be studied by the serious student of computer science. The book should therefore be most suited to those undergraduate programmes that put the emphasis on such areas as programming language semantics, program correctness, and algorithm analysis and design.
Book Synopsis Mathematical Tools for Applied Multivariate Analysis by : Paul E. Green
Download or read book Mathematical Tools for Applied Multivariate Analysis written by Paul E. Green and published by Academic Press. This book was released on 2014-05-10 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical Tools for Applied Multivariate Analysis provides information pertinent to the aspects of transformational geometry, matrix algebra, and the calculus that are most relevant for the study of multivariate analysis. This book discusses the mathematical foundations of applied multivariate analysis. Organized into six chapters, this book begins with an overview of the three problems in multiple regression, principal components analysis, and multiple discriminant analysis. This text then presents a standard treatment of the mechanics of matrix algebra, including definitions and operations on matrices, vectors, and determinants. Other chapters consider the topics of eigenstructures and linear transformations that are important to the understanding of multivariate techniques. This book discusses as well the eigenstructures and quadratic forms. The final chapter deals with the geometric aspects of linear transformations. This book is a valuable resource for students.
Book Synopsis Mathematical Foundations of Neuroscience by : G. Bard Ermentrout
Download or read book Mathematical Foundations of Neuroscience written by G. Bard Ermentrout and published by Springer Science & Business Media. This book was released on 2010-07-01 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book applies methods from nonlinear dynamics to problems in neuroscience. It uses modern mathematical approaches to understand patterns of neuronal activity seen in experiments and models of neuronal behavior. The intended audience is researchers interested in applying mathematics to important problems in neuroscience, and neuroscientists who would like to understand how to create models, as well as the mathematical and computational methods for analyzing them. The authors take a very broad approach and use many different methods to solve and understand complex models of neurons and circuits. They explain and combine numerical, analytical, dynamical systems and perturbation methods to produce a modern approach to the types of model equations that arise in neuroscience. There are extensive chapters on the role of noise, multiple time scales and spatial interactions in generating complex activity patterns found in experiments. The early chapters require little more than basic calculus and some elementary differential equations and can form the core of a computational neuroscience course. Later chapters can be used as a basis for a graduate class and as a source for current research in mathematical neuroscience. The book contains a large number of illustrations, chapter summaries and hundreds of exercises which are motivated by issues that arise in biology, and involve both computation and analysis. Bard Ermentrout is Professor of Computational Biology and Professor of Mathematics at the University of Pittsburgh. David Terman is Professor of Mathematics at the Ohio State University.
Book Synopsis A Mathematical Primer for Social Statistics by : John Fox
Download or read book A Mathematical Primer for Social Statistics written by John Fox and published by SAGE Publications. This book was released on 2021-01-11 with total page 199 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Mathematical Primer for Social Statistics, Second Edition presents mathematics central to learning and understanding statistical methods beyond the introductory level: the basic "language" of matrices and linear algebra and its visual representation, vector geometry; differential and integral calculus; probability theory; common probability distributions; statistical estimation and inference, including likelihood-based and Bayesian methods. The volume concludes by applying mathematical concepts and operations to a familiar case, linear least-squares regression. The Second Edition pays more attention to visualization, including the elliptical geometry of quadratic forms and its application to statistics. It also covers some new topics, such as an introduction to Markov-Chain Monte Carlo methods, which are important in modern Bayesian statistics. A companion website includes materials that enable readers to use the R statistical computing environment to reproduce and explore computations and visualizations presented in the text. The book is an excellent companion to a "math camp" or a course designed to provide foundational mathematics needed to understand relatively advanced statistical methods.
Author :Diethard Ernst Pallaschke Publisher :Springer Science & Business Media ISBN 13 :9401715882 Total Pages :597 pages Book Rating :4.4/5 (17 download)
Book Synopsis Foundations of Mathematical Optimization by : Diethard Ernst Pallaschke
Download or read book Foundations of Mathematical Optimization written by Diethard Ernst Pallaschke and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many books on optimization consider only finite dimensional spaces. This volume is unique in its emphasis: the first three chapters develop optimization in spaces without linear structure, and the analog of convex analysis is constructed for this case. Many new results have been proved specially for this publication. In the following chapters optimization in infinite topological and normed vector spaces is considered. The novelty consists in using the drop property for weak well-posedness of linear problems in Banach spaces and in a unified approach (by means of the Dolecki approximation) to necessary conditions of optimality. The method of reduction of constraints for sufficient conditions of optimality is presented. The book contains an introduction to non-differentiable and vector optimization. Audience: This volume will be of interest to mathematicians, engineers, and economists working in mathematical optimization.
Author :Anthony William Fairbank Edwards Publisher :Cambridge University Press ISBN 13 :9780521775441 Total Pages :138 pages Book Rating :4.7/5 (754 download)
Book Synopsis Foundations of Mathematical Genetics by : Anthony William Fairbank Edwards
Download or read book Foundations of Mathematical Genetics written by Anthony William Fairbank Edwards and published by Cambridge University Press. This book was released on 2000-01-13 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: A definitive account of the origins of modern mathematical population genetics, first published in 2000.
Book Synopsis Mathematical Foundation of Geodesy by : Kai Borre
Download or read book Mathematical Foundation of Geodesy written by Kai Borre and published by Springer Science & Business Media. This book was released on 2006-09-23 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains selected papers by Torben Krarup, one of the most important geodesists of the 20th century. The collection includes the famous booklet "A Contribution to the Mathematical Foundation of Physical Geodesy" from 1969, the unpublished "Molodenskij letters" from 1973, the final version of "Integrated Geodesy" from 1978, "Foundation of a Theory of Elasticity for Geodetic Networks" from 1974, as well as trend-setting papers on the theory of adjustment.
Book Synopsis Foundations of Data Science by : Avrim Blum
Download or read book Foundations of Data Science written by Avrim Blum and published by Cambridge University Press. This book was released on 2020-01-23 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.
