R is an open source programming language and interactive programming environment that has become the software tool of choice in data analytics. Learning Base R provides an introduction to the language for those with and without prior programming experience. It introduces the key topics that you will need to begin analyzing data and programming in R. The focus here is on the R language rather than a particular application. Nearly 200 exercises allow you to assess your understanding of R.
Transitioning to Calculus is a comprehensive compilation of the mathematical concepts and formulas that are required of students entering their first class in calculus. The essentials of arithmetic, algebra, geometry, analytic geometry, trigonometry, and complex variables are organized into separate chapters. The purpose of this book is to provide a succinct but comprehensive list of the topics required of students entering calculus.Over 100 figures highlight the intuitive and geometric aspects of the formulas and concepts. Each chapter ends with a series of exercises (with space provided for working out a solution) that are designed to reinforce the application of the concepts and formulas. Complete solutions to the problems are included.
This new edition includes the latest advances and developments in computational probability involving A Probability Programming Language (APPL). The book examines and presents, in a systematic manner, computational probability methods that encompass data structures and algorithms. The developed techniques address problems that require exact probability calculations, many of which have been considered intractable in the past. The book addresses the plight of the probabilist by providing algorithms to perform calculations associated with random variables. Computational Probability: Algorithms and Applications in the Mathematical Sciences, 2nd Edition begins with an introductory chapter that contains short examples involving the elementary use of APPL. Chapter 2 reviews the Maple data structures and functions necessary to implement APPL. This is followed by a discussion of the development of the data structures and algorithms (Chapters 3–6 for continuous random variables and Chapters 7–9 for discrete random variables) used in APPL. The book concludes with Chapters 10–15 introducing a sampling of various applications in the mathematical sciences. This book should appeal to researchers in the mathematical sciences with an interest in applied probability and instructors using the book for a special topics course in computational probability taught in a mathematics, statistics, operations research, management science, or industrial engineering department.
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