The Pleasures of Probability (Undergraduate Texts in Mathematics / Readings in Mathematics)
By Richard Isaac
* Publisher: Springer
* Number Of Pages: 268
* Publication Date: 1996-10-30
* ISBN-10 / ASIN: 038794415X
* ISBN-13 / EAN: 9780387944159
* Binding: Hardcover
Product Description:
The purpose of "The Pleasures of Probability" is to introduce some of the most fundamental ideas in classical probability to a fairly general audience - reaching from mathematical amateurs to scientists, from students to professional mathematicians. The only prerequisites required are a decent background in elementary algebra and an interest in discussions of a variety of problems and applications in probability. The style is informal, and the chapters are more like essays on a particular topic than textbook treatments. Even well-known problems are often covered in more depth than usual in order to illustrate underlying ideas. The book can be used as a text for a first course in probability or as a companion to a text. Each chapter ends with a few problems, the answers to which are given at the end of the book.
Summary: nice intro to probability
Rating: 5
Professor Isaac has written a book for those interested in learning about probability. It is at a high school algebra level although knowledge of calculus could be helpful at times. He starts with the now famous Monte Hall problem and provides the most lucid explanation I have seen to date. This is a great way to introduce important probability notions such as sample space and probability models for the sample outcomes. Deals mainly with discrete probability which is easiest to understand and yet rich with applications in gambling and other areas.
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
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