About the Book
This book provides a contemporary and lively postcalculus introduction to the subject of probability. The exposition reflects a desirable balance between fundamental theory and many applications involving a broad range of real problem scenarios. It is intended to appeal to a wide audience, including mathematics and statistics majors, prospective engineers and scientists, and those business and social science majors interested in the quantitative aspects of their disciplines. A one-term course would cover material in the core chapters (1-4), hopefully supplemented by selections from one or more of the remaining chapters on statistical inference (Ch. 5), Markov chains (Ch. 6), stochastic processes (Ch. 7), and signal processing (Ch. 8). The last chapter is specifically designed for electrical and computer engineers, making the book suitable for a one-term class on random signals and noise. Alternatively, there is certainly enough material for those lucky enough to be teaching or taking a year-long course.
Most of the core will be accessible to those who have taken a year of univariate differential and integral calculus; matrix algebra, multivariate calculus, and engineering mathematics are needed for the later, more advanced chapters. One unique feature of this book is the inclusion of sections that illustrate the importance of software for carrying out simulations when answers to questions cannot be obtained analytically; R and Matlab code are provided so that students can create their own simulations. Another feature that sets this book apart is the Introduction, which addresses the question "Why study probability?" by surveying selected examples from recent journal articles and discussing some classic problems whose solutions run counter to intuition. The book contains about 1100 exercises, ranging from straightforward to reasonably challenging; roughly 700 of these appear in the first four chapters. The book's preface provides more information about our purpose, content, mathematical level, and suggestions for what can be covered in courses of varying duration.
Table of Contents:
Probability.- Discrete Random Variables and Probability Distributions.- Continuous Random Variables and Probability Distributions.- Joint probability distributions and their applications.- The Basics of Statistical Inference.- Markov chains.- Random processes.- Introduction to signal processing.
About the Author :
Jay Devore received a B.S. in Engineering Science from the University of California, Berkeley, and a Ph.D. in Statistics from Stanford University. He previously taught at the University of Florida and Oberlin College, and has had visiting positions at Stanford, Harvard, the University of Washington, New York University, and most recently for five successive years at Columbia. He has been on the faculty at California Polytechnic State University, San Luis Obispo, since 1977, where he was chair of the Department of Statistics for seven years and recently achieved the exalted status of Professor Emeritus. Prior to getting involved in textbook writing, Jay published papers in the Journal of the American Statistical Association, the Journal of Applied Probability, Biometrika, Communications in Statistics, and the Annals of Statistics. He has previously authored or coauthored six other books, including the Springer book Modern Mathematical Statistics with Applications and Probability and Statistics for Engineering and the Sciences; the latter book won a McGuffey Longevity Award from the Text and Academic Authors Association for demonstrated excellence over time. He is a Fellow of the American Statistical Association, has been an associate editor for both the Journal of the American Statistical Association and The American Statistician, and received the Distinguished Teaching Award from Cal Poly in 1991. His recreational interests include reading, playing tennis, traveling, and cooking and eating good food. Matthew A. Carlton is Professor of Statistics at California Polytechnic State University, San Luis Obispo, where he joined the faculty in 1999. He received a B.A. in Mathematics from the University of California, Berkeley and a Ph.D. in Mathematics from the University of California, Los Angeles with an emphasis on pure and applied probability; his thesis research involved applications of the Poisson-Dirichlet random process. Matt has published papers in the Journal of Applied Probability, Human Biology, Journal of Statistics Education, and The American Statistician. He was also the lead content advisor for the "Statistically Speaking" video series, designed for community college statistics courses, and he has published a variety of educational materials for high school statistics teachers. Matt was responsible for developing both the applied probability course and the probability and random processes course at Cal Poly, which in turn inspired him to get involved in writing this text. His professional research focus involves applications of probability to genetics and engineering. Personal interests include travel, good wine, and college sports.
Review :
One unusual feature of this text is the use of software. This is used not only for the obvious number crunching applications, but also, perhaps more importantly, for simulations, which are used both to get answers and to illustrate concepts. This part of the book is bilingual, using both R (a favorite of statisticians) and Matlab (a favorite of many engineers)...This is an excellent textbook that should be considered by anyone offering or contemplating a course for which the level and topics covered are a good match. Robert W. Hayden, MAA Reviews, April, 2015 "This book is addressed to students of different branches (e.g., engineering, economics, computer science, mathematics, and so on) being in their sophomore or junior year and taking their first course on probability. It is addressed as well to tutors giving basic courses on stochastics. It is mainly a very good self-contained book of problems which introduces basic theoretical knowledge, necessary for solving these problems, and illustrates how to solve them." (Yana Kinderknecht, zbMATH 1311.60002, 2015)
