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...Show More
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.Show Less
Material for a one-term course in probability covered in chapters 1-4, with chapters 5-8 on topics like statistical inference and signal processing
Adopts a software-oriented approach with examples in R and MatLab?
Relevant?to wide audience that includes?mathematics?& statistics majors, prospective engineers?& scientists,?and?students in business?& the social sciences
Discrete Random Variables and Probability Distributions
Continuous Random Variables and Probability Distributions
Joint probability distributions and their applications
The Basics of Statistical Inference
Introduction to signal processing.
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)
ABOUT THE AUTHOR
- Bayesian and Frequentist Regression Methods Jon Wakefield
- Modern Mathematical Statistics with Applications Jay L. Devore, Kenneth N. Berk
- An Introduction to Statistical Learning Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
- Probability with Applications in Engineering, Science, and Technology Matthew A. Carlton, Jay L. Devore
- Introduction to the Practice of Statistics David S. Moore, George P. McCabe, Bruce A. Craig