- Table View
- List View
Introduction to Economic Analysis
by R. Preston Mcafee Tracy R. LewisThis book presents standard intermediate microeconomics material and some material that, in the authors' view, ought to be standard but is not. Introductory economics material is integrated. Standard mathematical tools, including calculus, are used throughout. The book easily serves as an intermediate microeconomics text, and can be used for a relatively sophisticated undergraduate who has not taken a basic university course in economics. The focus of this book is on the conceptual tools and not on fluff. As such, it reflects the approach actually adopted by the majority of economists for understanding economic activity. There are lots of models and equations, and no pictures of economists ;-) Economic analysis is used in many situations. When British Petroleum sets the price for Alaskan crude oil, it uses an estimated demand model, both for gasoline consumers and also for the refineries to which BP sells. Economic analysis was used by experts in the antitrust suit brought by the U.S. Department of Justice both to understand Microsoft s incentive to foreclose (eliminate from the market) rival Netscape and consumer behavior in the face of alleged foreclosure. Stock market analysts use economic models to forecast the profits of companies to predict the price of their stocks. When the government forecasts the budget deficit or considers a change in environmental regulations, it uses economic models. This book presents the building blocks of the models in common use by an army of economists thousands of times per day. This book, plus econometrics, provides most of the economic analysis tools to take upper division economics courses of any type.
Introduction to Econophysics: Contemporary Approaches with Python Simulations
by Carlo Requião da CunhaEconophysics explores the parallels between physics and economics and is an exciting topic that is attracting increasing attention. However there is a lack of literature that explains the topic from a broad perspective. This book introduces advanced undergraduates and graduate students in physics and engineering to the topic from this outlook, and is accompanied by rigorous mathematics which ensures that this will also be a good guide for established researchers in the field as well as researchers from other fields, such as mathematics and statistics, who are interested in the topic. Key features: Presents a multidisciplinary approach that will be of interest to students and researchers from physics, engineering, mathematics, statistics, and other physical sciences Accompanied by Python code with further learning opportunities, available for readers to download from the CRC Press website. Accessible to both students and researchers Carlo R. da Cunha is an associate professor of physics and engineering physics at the Universidade Federal do Rio Grande do Sul (Brazil) and has been since 2011. Dr. da Cunha received his M.Sc. Degree from the West Virginia University in 2001 and his Ph.D. degree from Arizona State University in 2005. He was a postdoctoral researcher at McGill University in Canada in 2006 and an assistant professor of engineering at the University Federal de Santa Catarina between 2007 and 2011. He has been a guest professor at the Technische Universität Wien (Austria), Chiba University (Japan) and Arizona State University (US). His research revolves around the physics of complex systems where he has been drawing parallels between physical and economic systems from quantum to social levels.
Introduction to Einstein’s Theory of Relativity: From Newton’s Attractive Gravity to the Repulsive Gravity of Vacuum Energy (Undergraduate Texts in Physics)
by Øyvind GrønThe revised and updated 2nd edition of this established textbook provides a self-contained introduction to the general theory of relativity, describing not only the physical principles and applications of the theory, but also the mathematics needed, in particular the calculus of differential forms.Updated throughout, the book contains more detailed explanations and extended discussions of several conceptual points, and strengthened mathematical deductions where required. It includes examples of work conducted in the ten years since the first edition of the book was published, for example the pedagogically helpful concept of a "river of space" and a more detailed discussion of how far the principle of relativity is contained in the general theory of relativity. Also presented is a discussion of the concept of the 'gravitational field' in Einstein's theory, and some new material concerning the 'twin paradox' in the theory of relativity. Finally, the book contains a new section about gravitational waves, exploring the dramatic progress in this field following the LIGO observations. Based on a long-established masters course, the book serves advanced undergraduate and graduate level students, and also provides a useful reference for researchers.
Introduction to Elementary Computational Modeling: Essential Concepts, Principles, and Problem Solving (Chapman & Hall/CRC Computational Science)
by Jose GarridoWith an emphasis on problem solving, this book introduces the basic principles and fundamental concepts of computational modeling. It emphasizes reasoning and conceptualizing problems, the elementary mathematical modeling, and the implementation using computing concepts and principles. Examples are included that demonstrate the computation and visu
Introduction to Engineering Analysis: Applying Algebra II to Solve Engineering Problems
by Edmund TsangIntroduction to Engineering Analysis: Applying Algebra II to Solve Engineering Problems
Introduction to Engineering Statistics and Lean Six Sigma: Statistical Quality Control and Design of Experiments and Systems
by Theodore T. AllenThis book provides an accessible one-volume introduction to Lean Six Sigma and statistics in engineering for students and industry practitioners. Lean production has long been regarded as critical to business success in many industries. Over the last ten years, instruction in Six Sigma has been linked more and more with learning about the elements of lean production. Building on the success of the first and second editions, this book expands substantially on major topics of increasing relevance to organizations interested in Lean Six Sigma. Each chapter includes summaries and review examples plus problems with their solutions. As well as providing detailed definitions and case studies of all Six Sigma methods, the book uniquely describes the relationship between operations research techniques and Lean Six Sigma. Further, this new edition features more introductory material on probability and inference and information about Deming's philosophy, human factors engineering, and the motivating potential score – the material is tied more directly to the Certified Quality Engineer (CQE) exam. New sections that explore motivation and change management, which are critical subjects for achieving valuable results have also been added. The book examines in detail Design For Six Sigma (DFSS), which is critical for many organizations seeking to deliver desirable products. It covers reliability, maintenance, and product safety, to fully span the CQE body of knowledge. It also incorporates recently emerging formulations of DFSS from industry leaders and offers more introductory material on experiment design, and includes practical experiments that will help improve students’ intuition and retention. The emphasis on lean production, combined with recent methods relating to DFSS, makes this book a practical, up-to-date resource for advanced students, educators and practitioners.
Introduction to Engineering and Scientific Computing with Python
by David E. Clough Steven C. ChapraAs more and more engineering departments and companies choose to use Python, this book provides an essential introduction to this open-source, free-to-use language. Expressly designed to support first-year engineering students, this book covers engineering and scientific calculations, Python basics, and structured programming. Based on extensive teaching experience, the text uses practical problem solving as a vehicle to teach Python as a programming language. By learning computing fundamentals in an engaging and hands-on manner, it enables the reader to apply engineering and scientific methods with Python, focusing this general language to the needs of engineers and the problems they are required to solve on a daily basis. Rather than inundating students with complex terminology, this book is designed with a leveling approach in mind, enabling students at all levels to gain experience and understanding of Python. It covers such topics as structured programming, graphics, matrix operations, algebraic equations, differential equations, and applied statistics. A comprehensive chapter on working with data brings this book to a close. This book is an essential guide to Python, which will be relevant to all engineers, particularly undergraduate students in their first year. It will also be of interest to professionals and graduate students looking to hone their programming skills, and apply Python to engineering and scientific contexts.
Introduction to Enumerative and Analytic Combinatorics (Discrete Mathematics and Its Applications)
by Miklos BonaIntroduction to Enumerative and Analytic Combinatorics fills the gap between introductory texts in discrete mathematics and advanced graduate texts in enumerative combinatorics. The book first deals with basic counting principles, compositions and partitions, and generating functions. It then focuses on the structure of permutations, graph enumerat
Introduction to Enumerative and Analytic Combinatorics (Discrete Mathematics and Its Applications)
by Miklos BonaThis award-winning textbook targets the gap between introductory texts in discrete mathematics and advanced graduate texts in enumerative combinatorics. The author’s goal is to make combinatorics more accessible to encourage student interest and to expand the number of students studying this rapidly expanding field.The book first deals with basic counting principles, compositions and partitions, and generating functions. It then focuses on the structure of permutations, graph enumeration, and extremal combinatorics. Lastly, the text discusses supplemental topics, including error-correcting codes, properties of sequences, and magic squares.Updates to the Third Edition include: Quick Check exercises at the end of each section, which are typically easier than the regular exercises at the end of each chapter. A new section discussing the Lagrange Inversion Formula and its applications, strengthening the analytic flavor of the book. An extended section on multivariate generating functions. Numerous exercises contain material not discussed in the text allowing instructors to extend the time they spend on a given topic. A chapter on analytic combinatorics and sections on advanced applications of generating functions, demonstrating powerful techniques that do not require the residue theorem or complex integration, and extending coverage of the given topics are highlights of the presentation.The second edition was recognized as an Outstanding Academic Title of the Year by Choice Magazine, published by the American Library Association.
Introduction to Environmental Data Science (Chapman & Hall/CRC Data Science Series)
by Jerry DavisIntroduction to Environmental Data Science focuses on data science methods in the R language applied to environmental research, with sections on exploratory data analysis in R including data abstraction, transformation, and visualization; spatial data analysis in vector and raster models; statistics and modelling ranging from exploratory to modelling, considering confirmatory statistics and extending to machine learning models; time series analysis, focusing especially on carbon and micrometeorological flux; and communication. Introduction to Environmental Data Science is an ideal textbook to teach undergraduate to graduate level students in environmental science, environmental studies, geography, earth science, and biology, but can also serve as a reference for environmental professionals working in consulting, NGOs, and government agencies at the local, state, federal, and international levels. Features • Gives thorough consideration of the needs for environmental research in both spatial and temporal domains. • Features examples of applications involving field-collected data ranging from individual observations to data logging. • Includes examples also of applications involving government and NGO sources, ranging from satellite imagery to environmental data collected by regulators such as EPA. • Contains class-tested exercises in all chapters other than case studies. Solutions manual available for instructors. • All examples and exercises make use of a GitHub package for functions and especially data.
Introduction to Experimental Designs with PROC GLIMMIX of SAS: Applications in Food Science and Agricultural Science
by Osval Antonio Montesinos López Josafhat Salinas Ruíz Jose CrossaIn this book, the subject of design and analysis of experiments has been covered in simple language by giving basic concepts of various designs and essential data analysis steps of designed experiments. It has become clear that among researchers, mainly from the areas of food and agricultural sciences, there is a great need for a reference work on design and analysis of experiments that covers basic concepts, provides examples of varied situations that require the use of the experimental designs and that offers clear steps required for the correct analysis execution. This book covers such needs while also sharing codes in the Statistical Analysis Systems (SAS) for each of the designs covered using Proc Glimmix to perform the analysis. It is hoped that this will allow readers to directly analyze the data from their experiments.
Introduction to Experimental Mathematics
by Søren Eilers Rune JohansenMathematics is not, and never will be, an empirical science, but mathematicians are finding that the use of computers and specialized software allows the generation of mathematical insight in the form of conjectures and examples, which pave the way for theorems and their proofs. In this way, the experimental approach to pure mathematics is revolutionizing the way research mathematicians work. As the first of its kind, this book provides material for a one-semester course in experimental mathematics that will give students the tools and training needed to systematically investigate and develop mathematical theory using computer programs written in Maple. Accessible to readers without prior programming experience, and using examples of concrete mathematical problems to illustrate a wide range of techniques, the book gives a thorough introduction to the field of experimental mathematics, which will prepare students for the challenge posed by open mathematical problems.
Introduction to Financial Derivatives with Python (Chapman and Hall/CRC Financial Mathematics Series)
by Elisa Alòs Raúl MerinoIntroduction to Financial Derivatives with Python is an ideal textbook for an undergraduate course on derivatives, whether on a finance, economics, or financial mathematics programme. As well as covering all of the essential topics one would expect to be covered, the book also includes the basis of the numerical techniques most used in the financial industry, and their implementation in Python. Features Connected to a Github repository with the codes in the book. The repository can be accessed at https://bit.ly/3bllnuf Suitable for undergraduate students, as well as anyone who wants a gentle introduction to the principles of quantitative finance No pre-requisites required for programming or advanced mathematics beyond basic calculus
Introduction to Financial Forecasting in Investment Analysis
by John B. Guerard Jr.Forecasting--the art and science of predicting future outcomes--has become a crucial skill in business and economic analysis. This volume introduces the reader to the tools, methods, and techniques of forecasting, specifically as they apply to financial and investing decisions. With an emphasis on "earnings per share" (eps), the author presents a data-oriented text on financial forecasting, understanding financial data, assessing firm financial strategies (such as share buybacks and R&D spending), creating efficient portfolios, and hedging stock portfolios with financial futures. The opening chapters explain how to understand economic fluctuations and how the stock market leads the general economic trend; introduce the concept of portfolio construction and how movements in the economy influence stock price movements; and introduce the reader to the forecasting process, including exponential smoothing and time series model estimations. Subsequent chapters examine the composite index of leading economic indicators (LEI); review financial statement analysis and mean-variance efficient portfolios; and assess the effectiveness of analysts' earnings forecasts. Using data from such firms as Intel, General Electric, and Hitachi, Guerard demonstrates how forecasting tools can be applied to understand the business cycle, evaluate market risk, and demonstrate the impact of global stock selection modeling and portfolio construction.
Introduction to Financial Mathematics (Advances in Applied Mathematics)
by Kevin J. HastingsThe second edition of this successful and widely recognized textbook again focuses on discrete topics. The author recognizes two distinct paths of study and careers of actuarial science and financial engineering. This text can be very useful as a common core for both. Therefore, there is substantial material in Introduction to Financial Mathematics, Second Edition on the theory of interest (the first half of the book), as well as the probabilistic background necessary for the study of portfolio optimization and derivative valuation (the second half). A course in multivariable calculus is not required.The material in the first two chapters should go a long way toward helping students prepare for the Financial Mathematics (FM) actuarial exam. Also, the discrete material will reveal how beneficial it is for the students to know more about loans in their personal financial lives.The notable changes and updates to this edition are itemized in the Preface, but overall, the presentation has been made more efficient. One example is the chapter on discrete probability, which is rather unique in its emphasis on giving the deterministic problems studied earlier a probabilistic context. The section on Markov chains, which is not essential to the development, has been scaled down. Sample spaces and probability measures, random variables and distributions, expectation, conditional probability, independence, and estimation all follow.Optimal portfolio selection coverage is reorganized and the section on the practicalities of stock transactions has been revised. Market portfolio and Capital Market Theory coverage is expanded. New sections on Swaps and Value-at-Risk have been added. This book, like the first edition, was written so that the print edition could stand alone. At times we simplify complicated algebraic expressions, or solve systems of linear equations, or numerically solve non-linear equations. Also, some attention is given to the use of computer simulation to approximate solutions to problems.
Introduction to Financial Mathematics (Advances in Applied Mathematics)
by Kevin J. HastingsIntroduction to Financial Mathematics is ideal for an introductory undergraduate course. Unlike most textbooks aimed at more advanced courses, the text motivates students through a discussion of personal finances and portfolio management. The author then goes on to cover valuation of financial derivatives in discrete time, using all of closed form,
Introduction to Financial Mathematics: With Computer Applications (Textbooks in Mathematics)
by Donald R. Chambers Qin LuThis book’s primary objective is to educate aspiring finance professionals about mathematics and computation in the context of financial derivatives. The authors offer a balance of traditional coverage and technology to fill the void between highly mathematical books and broad finance books. The focus of this book is twofold: To partner mathematics with corresponding intuition rather than diving so deeply into the mathematics that the material is inaccessible to many readers. To build reader intuition, understanding and confidence through three types of computer applications that help the reader understand the mathematics of the models. Unlike many books on financial derivatives requiring stochastic calculus, this book presents the fundamental theories based on only undergraduate probability knowledge. A key feature of this book is its focus on applying models in three programming languages –R, Mathematica and EXCEL. Each of the three approaches offers unique advantages. The computer applications are carefully introduced and require little prior programming background. The financial derivative models that are included in this book are virtually identical to those covered in the top financial professional certificate programs in finance. The overlap of financial models between these programs and this book is broad and deep.
Introduction to Financial Models for Management and Planning
by James R. Morris John P. DaleyA properly structured financial model can provide decision makers with a powerful planning tool that helps them identify the consequences of their decisions before they are put into practice. Introduction to Financial Models for Management and Planning, Second Edition enables professionals and students to learn how to develop and use computer-based models for financial planning. This volume provides critical tools for the financial toolbox, then shows how to use them tools to build successful models.
Introduction to Finite Element Analysis: A Textbook for Engineering Students
by S. Unnikrishnan Nair S. SomanathThis textbook covers the basic concepts and applications of finite element analysis. It is specifically aimed at introducing this advanced topic to undergraduate-level engineering students and practicing engineers in a lucid manner. It also introduces a structural and heat transfer analysis software FEASTSMT which has wide applications in civil, mechanical, nuclear and automobile engineering domains. This software has been developed by generations of scientists and engineers of Vikram Sarabhai Space Centre and Indian Space Research Organisation. Supported with many illustrative examples, the textbook covers the classical methods of estimating solutions of mathematical models. The book is written in an easy-to-understand manner. This textbook also contains numeral exercise problems to aid self-learning of the students. The solutions to these problems are demonstrated using finite element software. Furthermore, the textbook contains several tutorials and associated online resources on usage of the FEASTSMT software. Given the contents, this textbook is highly useful for the undergraduate students of various disciplines of engineering. It is also a good reference book for the practicing engineers.
Introduction to Finite Element Vibration Analysis
by Maurice PetytThis is an introduction to the mathematical basis of finite element analysis as applied to vibrating systems. Finite element analysis is a technique that is very important in modeling the response of structures to dynamic loads. Although this book assumes no previous knowledge of finite element methods, those who do have knowledge will still find the book to be useful. It can be utilised by aeronautical, civil, mechanical, and structural engineers as well as naval architects. This second edition includes information on the many developments that have taken place over the last twenty years. Existing chapters have been expanded where necessary, and three new chapters have been included that discuss the vibration of shells and multi-layered elements and provide an introduction to the hierarchical finite element method.
Introduction to Finite and Spectral Element Methods Using MATLAB
by Constantine PozrikidisIncorporating new topics and original material, Introduction to Finite and Spectral Element Methods Using MATLAB, Second Edition enables readers to quickly understand the theoretical foundation and practical implementation of the finite element method and its companion spectral element method. Readers gain hands-on computational experience by using
Introduction to Fluid Dynamics
by Edward B. Mcleod Jr.Concise, unified, and logical, this introduction to the study of the basic principles of fluid dynamics emphasizes the statement of problems in mathematical language. In addition to its value as a reference for professional engineers, this volume is suitable for advanced undergraduates and graduate students of mathematics and engineering. Some familiarity with the algebra of vector fields is assumed, and a useful appendix provides a succinct review of vector algebra.An introductory chapter covers fundamental notions from the continuum hypothesis to steady-state flow. Succeeding chapters explore conservation of mass, forces acting on a fluid in equilibrium, dynamic equations of motion, irrotational motion, integration of Euler's equation in special cases, and flows representable by harmonic functions. Additional topics include two dimensional flows, rectilinear vortices, general vortex motion, flows with a free boundary, and compressible fluids.
Introduction to Formal Languages (Dover Books on Mathematics)
by György E. RévészThis highly technical introduction to formal languages in computer science covers all areas of mainstream formal language theory, including such topics as operations on languages, context-sensitive languages, automata, decidability, syntax analysis, derivation languages, and more. Geared toward advanced undergraduates and graduate students, the treatment examines mathematical topics related to mathematical logic, set theory, and linguistics. All subjects are integral to the theory of computation.Numerous worked examples appear throughout the book, and end-of-chapter exercises enable readers to apply theory and methods to real-life problems. Elegant mathematical proofs are provided for almost all theorems.
Introduction to Formal Philosophy (Springer Undergraduate Texts In Philosophy Ser.)
by Vincent F. Hendricks Sven Ove Hansson Esther Michelsen KjeldahlThis Undergraduate Textbook introduces key methods and examines the major areas of philosophy in which formal methods play pivotal roles. Coverage begins with a thorough introduction to formalization and to the advantages and pitfalls of formal methods in philosophy. The ensuing chapters show how to use formal methods in a wide range of areas. Throughout, the contributors clarify the relationships and interdependencies between formal and informal notions and constructions. Their main focus is to show how formal treatments of philosophical problems may help us understand them better. Formal methods can be used to solve problems but also to express new philosophical problems that would never have seen the light of day without the expressive power of the formal apparatus. Formal philosophy merges work in different areas of philosophy as well as logic, mathematics, computer science, linguistics, physics, psychology, biology, economics, political theory, and sociology. This title offers an accessible introduction to this new interdisciplinary research area to a wide academic audience.
Introduction to Foundation Models
by Pin-Yu Chen Sijia LiuThis book offers an extensive exploration of foundation models, guiding readers through the essential concepts and advanced topics that define this rapidly evolving research area. Designed for those seeking to deepen their understanding and contribute to the development of safer and more trustworthy AI technologies, the book is divided into three parts providing the fundamentals, advanced topics in foundation modes, and safety and trust in foundation models: Part I introduces the core principles of foundation models and generative AI, presents the technical background of neural networks, delves into the learning and generalization of transformers, and finishes with the intricacies of transformers and in-context learning. Part II introduces automated visual prompting techniques, prompting LLMs with privacy, memory-efficient fine-tuning methods, and shows how LLMs can be reprogrammed for time-series machine learning tasks. It explores how LLMs can be reused for speech tasks, how synthetic datasets can be used to benchmark foundation models, and elucidates machine unlearning for foundation models. Part III provides a comprehensive evaluation of the trustworthiness of LLMs, introduces jailbreak attacks and defenses for LLMs, presents safety risks when find-tuning LLMs, introduces watermarking techniques for LLMs, presents robust detection of AI-generated text, elucidates backdoor risks in diffusion models, and presents red-teaming methods for diffusion models. Mathematical notations are clearly defined and explained throughout, making this book an invaluable resource for both newcomers and seasoned researchers in the field.