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Probability and mathematical statistics : theory, applications, and practice in R
Meyer M., SIAM-Society for Industrial and Applied Mathematics, Philadelphia, PA, 2019. 707 pp.  Type: Book (978-1-611975-77-2)
Date Reviewed: Jan 7 2020

Back when I was an undergraduate, I took a course in numerical recipes (in the C programming language). That course introduced me to the approximation of answers to questions that, often times, cannot be solved through analytical means. My sense is that this book will do the same for readers who need a thorough grounding in numerical recipes for understanding probability and statistics empirically. R is a wonderful programming language for statistical recipes.

Early on in the book, the author shows how to use R to perform simulations that can help answer questions on probability in an empirical manner and check analytically computed answers, and generally encourages the reader to use powerful artifacts of the language in understanding probability and statistics much better. A word of caution: this book will not teach the reader how to use R, but will make an existing R user far more conversant with the language of probability and statistics.

The book posits that probability and statistics are two faces of the same coin; they are related to each other. Thus, while probability is interested in quantifying the occurrence of an event, statistics is more interested in estimating the parameters of distribution that lead to the occurrence of that event. Following this dichotomy, the book can roughly be divided into two parts, with the first part focusing on probability and the latter on statistics. While the book has a brief introductory chapter on probability, it does assume that the reader is fairly conversant with discrete and continuous mathematics, which is used liberally throughout the book. There are 60 chapters across 524 pages, which means that each chapter is short (about eight pages) and focuses on one particular aspect. Such a setup invites the reader to explore the subject empirically only after it’s understood analytically. An added benefit of the book is that each chapter includes a set of questions, and more than 700 answers to the odd-numbered questions.

In summary, this is an excellent title to have on your bookshelf. It is not intended to serve as a textbook as much as it is designed to serve as a reference for practitioners and as a self-study guide for graduate students.

Reviewer:  Vijay Gurbani Review #: CR146827 (2005-0099)
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Probability And Statistics (G.3 )
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