Математика
Различные книги в жанре МатематикаApplied Mathematics for the Analysis of Biomedical Data. Models, Methods, and MATLAB
Features a practical approach to the analysis of biomedical data via mathematical methods and provides a MATLAB® toolbox for the collection, visualization, and evaluation of experimental and real-life data Applied Mathematics for the Analysis of Biomedical Data: Models, Methods, and MATLAB® presents a practical approach to the task that biological scientists face when analyzing data. The primary focus is on the application of mathematical models and scientific computing methods to provide insight into the behavior of biological systems. The author draws upon his experience in academia, industry, and government–sponsored research as well as his expertise in MATLAB to produce a suite of computer programs with applications in epidemiology, machine learning, and biostatistics. These models are derived from real–world data and concerns. Among the topics included are the spread of infectious disease (HIV/AIDS) through a population, statistical pattern recognition methods to determine the presence of disease in a diagnostic sample, and the fundamentals of hypothesis testing. In addition, the author uses his professional experiences to present unique case studies whose analyses provide detailed insights into biological systems and the problems inherent in their examination. The book contains a well-developed and tested set of MATLAB functions that act as a general toolbox for practitioners of quantitative biology and biostatistics. This combination of MATLAB functions and practical tips amplifies the book’s technical merit and value to industry professionals. Through numerous examples and sample code blocks, the book provides readers with illustrations of MATLAB programming. Moreover, the associated toolbox permits readers to engage in the process of data analysis without needing to delve deeply into the mathematical theory. This gives an accessible view of the material for readers with varied backgrounds. As a result, the book provides a streamlined framework for the development of mathematical models, algorithms, and the corresponding computer code. In addition, the book features: Real–world computational procedures that can be readily applied to similar problems without the need for keen mathematical acumen Clear delineation of topics to accelerate access to data analysis Access to a book companion website containing the MATLAB toolbox created for this book, as well as a Solutions Manual with solutions to selected exercises Applied Mathematics for the Analysis of Biomedical Data: Models, Methods, and MATLAB® is an excellent textbook for students in mathematics, biostatistics, the life and social sciences, and quantitative, computational, and mathematical biology. This book is also an ideal reference for industrial scientists, biostatisticians, product development scientists, and practitioners who use mathematical models of biological systems in biomedical research, medical device development, and pharmaceutical submissions.
Efficiency and Productivity Growth. Modelling in the Financial Services Industry
An authoritative introduction to efficiency and productivity analysis with applications in both the banking and finance industry In light of the recent global financial crisis, several studies have examined the efficiency of financial institutions. A number of open questions remain and this book reviews recent issues and state-of-the-art techniques in the assessment of the efficiency and productivity of financial institutions. Written by an international team of experts, the first part of the book links efficiency with a variety of topics like Latin American banking, market discipline and governance, economics of scale, off-balance-sheet activities, productivity of foreign banks, mergers and acquisitions, and mutual fund ratings. The second part of the book compares existing techniques and state-of-the-art techniques in the bank efficiency literature, including among others, network data envelopment analysis and quantile regression. The book is suitable for academics and professionals as well as postgraduate research students working in banking and finance. Efficiency and Productivity Growth: Provides an authoritative introduction to efficiency and productivity analysis with applications in both the banking and mutual funds industry such as efficiency of Asian banks, cooperatives and not-for-profit credit associations. Explores contemporary research issues in the area of efficiency and productivity measurement in the financial sector. Evaluates the most suitable approaches to selecting inputs and outputs as well as selecting the most efficient techniques, such as parametric and non-parametric, to estimate the models.
Solutions Manual to Accompany Linear Algebra. Ideas and Applications
This Student Solutions Manual to Accompany Linear Algebra: Ideas and Applications, Fourth Edition contains solutions to the odd numbered problems to further aid in reader comprehension, and an Instructor's Solutions Manual (inclusive of suggested syllabi) is available via written request to the Publisher. Both the Student and Instructor Manuals have been enhanced with further discussions of the applications sections, which is ideal for readers who wish to obtain a deeper knowledge than that provided by pure algorithmic approaches. Linear Algebra: Ideas and Applications, Fourth Edition provides a unified introduction to linear algebra while reinforcing and emphasizing a conceptual and hands-on understanding of the essential ideas. Promoting the development of intuition rather than the simple application of methods, this book successfully helps readers to understand not only how to implement a technique, but why its use is important.
Linear Algebra. Ideas and Applications
Praise for the Third Edition “This volume is ground-breaking in terms of mathematical texts in that it does not teach from a detached perspective, but instead, looks to show students that competent mathematicians bring an intuitive understanding to the subject rather than just a master of applications.” – Electric Review A comprehensive introduction, Linear Algebra: Ideas and Applications, Fourth Edition provides a discussion of the theory and applications of linear algebra that blends abstract and computational concepts. With a focus on the development of mathematical intuition, the book emphasizes the need to understand both the applications of a particular technique and the mathematical ideas underlying the technique. The book introduces each new concept in the context of an explicit numerical example, which allows the abstract concepts to grow organically out of the necessity to solve specific problems. The intuitive discussions are consistently followed by rigorous statements of results and proofs. Linear Algebra: Ideas and Applications, Fourth Edition also features: Two new and independent sections on the rapidly developing subject of wavelets A thoroughly updated section on electrical circuit theory Illuminating applications of linear algebra with self-study questions for additional study End-of-chapter summaries and sections with true-false questions to aid readers with further comprehension of the presented material Numerous computer exercises throughout using MATLAB® code Linear Algebra: Ideas and Applications, Fourth Edition is an excellent undergraduate-level textbook for one or two semester courses for students majoring in mathematics, science, computer science, and engineering. With an emphasis on intuition development, the book is also an ideal self-study reference.
Machine Learning. Hands-On for Developers and Technical Professionals
Dig deep into the data with a hands-on guide to machine learning Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant, explaining how it works and how it is used within certain industries, allowing readers to incorporate the presented techniques into their own work as they follow along. A core tenant of machine learning is a strong focus on data preparation, and a full exploration of the various types of learning algorithms illustrates how the proper tools can help any developer extract information and insights from existing data. The book includes a full complement of Instructor's Materials to facilitate use in the classroom, making this resource useful for students and as a professional reference. At its core, machine learning is a mathematical, algorithm-based technology that forms the basis of historical data mining and modern big data science. Scientific analysis of big data requires a working knowledge of machine learning, which forms predictions based on known properties learned from training data. Machine Learning is an accessible, comprehensive guide for the non-mathematician, providing clear guidance that allows readers to: Learn the languages of machine learning including Hadoop, Mahout, and Weka Understand decision trees, Bayesian networks, and artificial neural networks Implement Association Rule, Real Time, and Batch learning Develop a strategic plan for safe, effective, and efficient machine learning By learning to construct a system that can learn from data, readers can increase their utility across industries. Machine learning sits at the core of deep dive data analysis and visualization, which is increasingly in demand as companies discover the goldmine hiding in their existing data. For the tech professional involved in data science, Machine Learning: Hands-On for Developers and Technical Professionals provides the skills and techniques required to dig deeper.
Entrepreneurial Finance. Fundamentals of Financial Planning and Management for Small Business
Featuring key topics within finance, small business management, and entrepreneurship to develop and maintain prosperous business ventures With a comprehensive and organized approach to fundamental financial theories, tools, and management techniques, Entrepreneurial Finance: Fundamentals of Financial Planning and Management for Small Business equips readers with the necessary fundamental knowledge and advanced skills to succeed in small firm and business settings. With a unique combination of topics from finance, small business management, and entrepreneurship, the book prepares readers for the challenges of today’s economy. Entrepreneurial Finance: Fundamentals of Financial Planning and Management for Small Business begins with key concepts of small business management and entrepreneurship, including management tools and techniques needed to establish, run, and lead business ventures. The book then delves into how small businesses are operated, managed, and controlled. General finance skills and methods are integrated throughout, and the book also features: Numerous practical examples and scenarios that provide a real-world perspective on entrepreneurship and small business management A brief summary, list of key concepts, and ten discussion questions at the end of each chapter to prepare readers for the challenges of today's economy A practical guide to the complete life of a small business, from establishing a new venture to training and developing young entrepreneurs tasked with maintaining and developing a prosperous economy An in-depth discussion of the entire process of writing a successful business plan, including the rationale, significance, and requirements Techniques needed to solidify the free enterprise tradition, develop entrepreneurial strategies, and grow small businesses Entrepreneurial Finance: Fundamentals of Financial Planning and Management for Small Business is an ideal textbook for upper-undergraduate and first-year graduate courses in entrepreneurial finance within business, economics, management science, and public administration departments. The book is also useful for MBA-level courses as well as for business and management PhD majors as a resource in methodology. The book is also an idea reference for entrepreneurs, business managers, market analysts, and decision makers who require information about the theoretical and quantitative aspects of entrepreneurial finance.
Classic Topics on the History of Modern Mathematical Statistics. From Laplace to More Recent Times
"There is nothing like it on the market…no others are as encyclopedic…the writing is exemplary: simple, direct, and competent." —George W. Cobb, Professor Emeritus of Mathematics and Statistics, Mount Holyoke College Written in a direct and clear manner, Classic Topics on the History of Modern Mathematical Statistics: From Laplace to More Recent Times presents a comprehensive guide to the history of mathematical statistics and details the major results and crucial developments over a 200-year period. Presented in chronological order, the book features an account of the classical and modern works that are essential to understanding the applications of mathematical statistics. Divided into three parts, the book begins with extensive coverage of the probabilistic works of Laplace, who laid much of the foundations of later developments in statistical theory. Subsequently, the second part introduces 20th century statistical developments including work from Karl Pearson, Student, Fisher, and Neyman. Lastly, the author addresses post-Fisherian developments. Classic Topics on the History of Modern Mathematical Statistics: From Laplace to More Recent Times also features: A detailed account of Galton's discovery of regression and correlation as well as the subsequent development of Karl Pearson's X2 and Student's t A comprehensive treatment of the permeating influence of Fisher in all aspects of modern statistics beginning with his work in 1912 Significant coverage of Neyman–Pearson theory, which includes a discussion of the differences to Fisher’s works Discussions on key historical developments as well as the various disagreements, contrasting information, and alternative theories in the history of modern mathematical statistics in an effort to provide a thorough historical treatment Classic Topics on the History of Modern Mathematical Statistics: From Laplace to More Recent Times is an excellent reference for academicians with a mathematical background who are teaching or studying the history or philosophical controversies of mathematics and statistics. The book is also a useful guide for readers with a general interest in statistical inference.
Statistical Data Analytics. Foundations for Data Mining, Informatics, and Knowledge Discovery, Solutions Manual
Solutions Manual to accompany Statistical Data Analytics: Foundations for Data Mining, Informatics, and Knowledge Discovery A comprehensive introduction to statistical methods for data mining and knowledge discovery. Extensive solutions using actual data (with sample R programming code) are provided, illustrating diverse informatic sources in genomics, biomedicine, ecological remote sensing, astronomy, socioeconomics, marketing, advertising and finance, among many others.
Statistical Data Analytics. Foundations for Data Mining, Informatics, and Knowledge Discovery
A comprehensive introduction to statistical methods for data mining and knowledge discovery. Applications of data mining and ‘big data’ increasingly take center stage in our modern, knowledge-driven society, supported by advances in computing power, automated data acquisition, social media development and interactive, linkable internet software. This book presents a coherent, technical introduction to modern statistical learning and analytics, starting from the core foundations of statistics and probability. It includes an overview of probability and statistical distributions, basics of data manipulation and visualization, and the central components of standard statistical inferences. The majority of the text extends beyond these introductory topics, however, to supervised learning in linear regression, generalized linear models, and classification analytics. Finally, unsupervised learning via dimension reduction, cluster analysis, and market basket analysis are introduced. Extensive examples using actual data (with sample R programming code) are provided, illustrating diverse informatic sources in genomics, biomedicine, ecological remote sensing, astronomy, socioeconomics, marketing, advertising and finance, among many others. Statistical Data Analytics: Focuses on methods critically used in data mining and statistical informatics. Coherently describes the methods at an introductory level, with extensions to selected intermediate and advanced techniques. Provides informative, technical details for the highlighted methods. Employs the open-source R language as the computational vehicle – along with its burgeoning collection of online packages – to illustrate many of the analyses contained in the book. Concludes each chapter with a range of interesting and challenging homework exercises using actual data from a variety of informatic application areas. This book will appeal as a classroom or training text to intermediate and advanced undergraduates, and to beginning graduate students, with sufficient background in calculus and matrix algebra. It will also serve as a source-book on the foundations of statistical informatics and data analytics to practitioners who regularly apply statistical learning to their modern data.
MATLAB und Mathematik kompetent einsetzen. Eine Einführung für Ingenieure und Naturwissenschaftler
Das erfolgreiche MATLAB- und Mathematikbuch von Stefan Adam fordert den Aufbau einer fundierten Kompetenz zur Losung von naturwissenschaftlichen und technischen Berechnungsproblemen. Zusammen mit den Erlauterungen zur Anwendung und Programmierung vom MATLAB wird immer auch ein tiefer Einblick vermittelt in die dahinter stehenden mathematischen Zusammenhange. Durch viele Beispiele, Ubungen und selbst zu erstellende Demonstrationsprogramme wird der Leser angeleitet, sich in der Umgebung von MATLAB kreativ zu bewegen. Das von einem MATrix-LABoratorium ausgegangene Softwarepaket hat sich langst zu einem Mathematik-Laboratorium weiterentwickelt, das weltweit an Universitaten sowie in Forschungs- und Entwicklungsabteilungen eine Spitzenstellung einnimmt. Die zwei Komponenten dieses Buches verstarken sich gegenseitig. Der mathematische Hintergrund fordert einerseits die Merkfahigkeit fur die Programmierstrukturen sowie die Entscheidungskompetenz zur Auswahl des besten Berechnungsablaufes. Selbstprogrammierte Losungsverfahren mit vielfaltigen grafischen Darstellungen vertiefen andererseits das Verstandnis fur oft abstrakte mathematische Zusammenhange. Fur das Arbeiten mit diesem Buch werden weder Vorkenntnisse einer Programmiersprache noch solche zu MATLAB benotigt. Mathematische Themen starten auf dem Niveau, das etwa ein Jahr vor dem Abitur erreicht wird, und steigen in sanften Stufen bis zu den Anforderungen der ersten vier Semester eines Naturwissenschafts- oder Ingenieurstudiums. Starke Querbezuge zu praktischen Problemen und hilfreiche bildhafte Vorstellungen machen die hier prasentierte Mathematik leichter verdaulich. Merkpunkte, Checklisten und Selbst-Tests dienen der Festigung der erworbenen Fahigkeiten und machen das Buch auch hervorragend zum Selbststudium geeignet. * In dieser zweiten Auflage konnen Teile des ersten Kapitels als MATLAB Crash-Kurs fur Ungeduldige oder fur Wiedereinsteiger dienen. * Hinweise auf Anwendungen der Toolbox zum Symbolischen Rechnen, also zum Bestimmen einer analytischen Losung oder zum Umsetzen von Formeln, finden sich uber das ganze Buch verteilt. * Im Internet ist unter www.wiley-vch.de/textbooks/ eine Fulle von erganzendem Material erhaltlich