Introduction to Bayesian Statistics, 2nd Edition

by William M. Bolstad

Praise for the First Edition "I cannot think of a better book for teachers of introductory statistics who want a readable and pedagogically sound text to introduce Bayesian statistics."
— Statistics in Medical Research "[This book] is written in a lucid conversational style, which is so rare in mathematical writings. It does an excellent job of presenting Bayesian statistics as a perfectly reasonable approach to elementary problems in statistics."
— The Magazine for Students of Statistics, American Statistical Association "Bolstad offers clear explanations of every concept and method making the book accessible and valuable to undergraduate and graduate students alike."
— Journal of Applied Statistics The use of Bayesian methods in applied statistical analysis has become increasingly popular, yet most introductory statistics texts continue to only present the subject using frequentist methods. Introduction to Bayesian Statistics, Second Edition focuses on Bayesian methods that can be used for inference, and it also addresses how these methods compare favorably with frequentist alternatives. Teaching statistics from the Bayesian perspective allows for direct probability statements about parameters, and this approach is now more relevant than ever due to computer programs that allow practitioners to work on problems that contain many parameters. This book uniquely covers the topics typically found in an introductory statistics book—but from a Bayesian perspective—giving readers an advantage as they enter fields where statistics is used. This Second Edition Introduction to Bayesian Statistics, Second Edition is an invaluable textbook for advanced undergraduate and graduate-level statistics courses as well as a practical reference for statisticians who require a working knowledge of Bayesian statistics.

Genres: Mathematics, Science, Textbooks, Reference

464 pages, Hardcover

First published January 1, 2004

Reviews

[The Emperor Napoleon · Rating 5 out of 5]

this has to be my favourite statistics book so far

[Fernando Guerra · Rating 5 out of 5]

I'm not a specialist on the topic, but I found this book to be highly illustrative on the bayesian approach to statistics, as well as the discipline of statistics in general. I think this approach is a richer, more complete way to treat statistics, viewing distribution parameters as random variables instead of the habitual, almost deterministic way.

I really liked the fact that it compares statistic inference in the habitual way with bayesian methods side by side, exposing pros and cons of each methodology in a clear and straightforward way. I'm by no means an expert on statistics after reading this, but I can think of myself as acquainted with this new approach on the discipline.

[Silvia Cardenas · Rating 3 out of 5]

No es un libro particularmente entretenido, pero entendí qué es inferencia bayesiana sin tener un background fuerte en estadística

[Benjamin Schneider · Rating 4 out of 5]

This book is essentially your traditional "Intro Stats" book, but based on a Bayesian approach to probability and with the term 'confidence interval' replaced with 'credibility interval.' The book is great at illustrating how the Bayesian building blocks (prior, likelihood, Bayes' rule, and posterior) are applied in different applications (single-parameter estimation, joint-parameter estimation, regression, etc.). The R code (found in the 'bolstad' package on CRAN, I believe) is quite nice and is excellent for simple applications such as single-parameter estimation.

[Tim Clouse · Rating 3 out of 5]

Good intro to Bayesian view. Not much ideology.

[Robb Seaton · Rating 2 out of 5]

Stylistically leaves a lot to be desired. Boring.

[J Chen]

Good introductory book