"This book covers practically all essential topics from modern probability and stochastic processes, which in one or another way are included in various university programs. So, is there something making this book different or better than many others available in the market? The answer is yes! The author presents the material systematically and rigorously. Of primary importance, however, is the emphasis on the modeling aspects. The large number of well-chosen and carefully described practical cases is a tremendous advantage of the book. … strongly recommended as a basic source for a variety of courses."
—G. Schröder, Zentralblatt MATH 1268
"The author’s mode of presentation is well chosen. New concepts are introduced with illuminating discussion and illustrations. … The book is physically nicely presented. … this text has something new to offer, mainly for business-oriented students, and I recommend that instructors give it serious consideration when choosing a text for their courses.
—Anthony G. Pakes, Mathematical Reviews, September 2013
"This is a superbly written text on probability and stochastic processes for students who have had two semesters of calculus and an introductory course in linear algebra. This includes upper division students in science and engineering including statistics and mathematics, as well as students in fields such as economics and finance. In addition, it will be a wonderful book for self study for many others. Important and well-chosen examples illustrate the theory throughout, and a large body of exercises supplements the text. It gives a lucid presentation of basic probability theory, including Markov chains and martingales. A special feature of this book is a marvelous exposition of many interesting aspects of financial mathematics that are generally considered rather intricate and inaccessible at this level. This book carries the imprint of a distinguished mathematician and teacher with expertise in probability theory and many of its special applications to mathematical economics and finance. It is an outstanding addition to the field requiring only a modest background in mathematics."
—Rabi Bhattacharya, Department of Mathematics, University of Arizona, Tucson, USA
"Written in a lively and stimulating manner, the book makes a very good impression. The author, having extensive teaching experience and an undoubted literary talent, has managed to create an original introduction to modern probability theory. The successful combination of a variety of examples, exercises and applications with deep and nontrivial ideas makes the book interesting not only for beginning students, but also for professionals working with probabilistic problems. I believe that the book can serve as an ideal textbook for anyone interested in probability theory and its applications. The book will take a worthy place in the literature on probabilistic issues."
—Youri Davydov, Laboratoire Paul Painlevé, Université des Sciences et Technologies de Lille, France
"The author has produced a comprehensive introduction to probability theory and stochastic processes, including martingales and Brownian motion. The text is suitable for students with a standard background in calculus and linear algebra. The approach is rigorous without being pedantic, and the text is liberally sprinkled with examples. Throughout, there is a welcome emphasis on stochastic modeling. Of note is the fairly early introduction and use of conditional expectations.
The main text is complemented by a large collection of exercises with a wide range of difficulty. The book is in fact two-in-one, as a series of ‘roadside markers’ guides the reader through two possible courses of study, one consisting of material suitable for a one-semester course, and the other a more in-depth journey suitable for a two-semester course. This book is a welcome and attractive addition to the list of textbooks available for an upper division probability course and would even be suitable for a graduate-level introduction to non-measure-theoretic probability and stochastic processes."
—Patrick J. Fitzsimmons, Department of Mathematics, University of California-San Diego, La Jolla, USA
