This calculus-based introduction to probability covers all of the traditional topics, along with a secondary emphasis on Monte Carlo simulation. Examples that introduce applications from a wide range of fields help the reader apply probability theory to real-world problems. The text covers all of the topics associated with Exam P given by the Society of Actuaries. Over 100 figures highlight the intuitive and geometric aspects of probability. Over 800 exercises are used to reinforce concepts and make this text appropriate for classroom use.
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 title organizes computational probability methods into a systematic treatment. The book examines two categories of problems. "Algorithms for Continuous Random Variables" covers data structures and algorithms, transformations of random variables, and products of independent random variables. "Algorithms for Discrete Random Variables" discusses data structures and algorithms, sums of independent random variables, and order statistics.
This title organizes computational probability methods into a systematic treatment. The book examines two categories of problems. "Algorithms for Continuous Random Variables" covers data structures and algorithms, transformations of random variables, and products of independent random variables. "Algorithms for Discrete Random Variables" discusses data structures and algorithms, sums of independent random variables, and order statistics.
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