Probability with R serves as a comprehensive first course in probability, using the freely available and downloadable programming language R to illustrate and clarify the principles involved.  Real examples show how probability can be applied to practical situations, with an emphasis on applications related to computing.

Promoting a simulation- and experimentation-driven methodology, this book highlights the relationship between probability and computing in five succinct parts:

·       The R Language introduces the reader to the essentials of the R language, including key procedures for summarizing and building graphical displays of statistical data.

·       Fundamentals of Probability provides the foundations of the basic concepts of probability and moves into applications in computing. Topics covered include conditional probability, Bayes' theorem, and systems reliability along with the development of the main laws and properties of probability.

·       Discrete Distributions addresses discrete random variables and their density and distribution functions, as well as properties of expectation. Each chapter leads with examples, which preview the material to follow. The geometric, binomial, hypergeometric and Poisson distributions are discussed, and used to develop sampling inspection schemes.

·       Continuous Distributions introduces continuous variables by examining the waiting time between Poisson occurrences. The exponential distribution and its applications to reliability and queues are investigated, and the Markov property is illustrated by simulation in R. The normal distribution is examined, and applied to statistical process control.

·       Tailing Off delves into the Markov and Chebyshev inequalities, as tools for estimating tail probabilities with limited information on the random variable.

Numerous exercises and projects are provided in each chapter, many of them requiring the use of R to perform routine calculations, or to conduct experiments with simulated data. The author directs the reader to the appropriate web-based resources for the installation of the R software package, and also supplies the essential commands for working in the R workspace.

With its accessible and hands-on approach, Probability with R is an ideal book for students in computer science, engineering and the general sciences at both undergraduate and graduate levels who require an introduction to probability, or who simply want to learn R. It also serves as a valuable reference for computing professionals who would like to further understand the relevance of probability to their disciplines.