Introduction to Bayesian Statistics

by William M. Bolstad

Traditionally, introductory statistics courses have been taught from a frequentist perspective. The recent upsurge in the use of Bayesian methods in applied statistical analysis highlights the need to expose students early on to the Bayes theorem, its advantages, and its applications. Based on the author’s successful courses, Introduction to Bayesian Statistics introduces statistics from a Bayesian perspective in a way that is understandable to readers with a reasonable mathematics background. Covering most of the same ground found in a typical statistics book–but from a Bayesian perspective–Introduction to Bayesian Statistics offers thorough, clearly-explained discussions To assist in the understanding of Bayesian statistics, this introduction provides readers with exercises (with selected answers); summaries of main points from each chapter; a calculus refresher, and a summary on the use of statistical tables; and R functions and Minitab macros for Bayesian analysis and Monte Carlo simulations (downloadable from the associated Web site)

Genres: Mathematics, Science, Textbooks, Reference

384 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