Important theory is presented but without the detailed mathematical proofs. Covers the gambler's ruin, geometric probability, Monte Carlo methods and some statistical decision theory. He also presents both the frequentist (throughout the text)and the Bayesian paradigms (Chapter 4) for statistical inference. Examples of the application of probability to statistical inference is nicely treated in Chapter 15. The deeper material on Markov chains and Brownian motion are relegated to the last two chapters (16 and 17). The exposition is excellent throughout and many good references are provided for readers who want to learn more or delve deeper into the theory.