physical examples. One especially useful feature of the chapter is the manner in which the author makes connections between uniform, Bernoulli, and Poison random variables.

summary of entropy and information theory and Chapter 10 concludes the main text of the book with a concise discussion of Markov chains. The two appendices summarize material that complements earlier chapters (Appendix A, random walks and Appendix B, memorylessness). In summary, Introduction to Probability with R is a well-organized course in probability theory. It presupposes that students are comfortable with programming and can pick up R from examples. Alternatively, a second text introducing R could be assigned and covered early in the semester. The text is most impressive in its clear treatment of advanced topics and as such is comparable to Elementary Probability Theory by Chung and AitSahlia (2003) or Strang’s (1988) treatment of linear algebra. In comparison to Chung’s text, Introduction to Probability in R will appeal more to sophisticated students with no specic preparation in mathematics beyond calculus but an interest in scientic research and a preference for hands-on investigation of probability theory through simulation in R.