A Student’s Guide to Bayesian Statistics

I waited until I got a couple of chapters into this book before I wrote a review. I must say that I am liking this book so far. It is just right for my situation where I am interested in using Bayesian analyses but it has been a few decades since I was a student in school. I appreciate the informal and direct way the author takes to introduce this topic and that he does not assume much prior knowledge for the reader. I would recommend this book to anyone just starting in Bayesian before you move on to more challenging texts in your studies.

Ben Lambert does a wonderful job of introducing the field of Bayesian analysis to motivated readers who have heard about the power of the Bayesian framework and tools, but who have not formally studied them. Section II provides the background on the concepts and mathematics behind the methodology, then Section III is the meat of ways to apply these methods to problems that many of us encounter in our research. This combination of foundational review and applied methods is unique to many of the Bayesian books I have examined. It even provides an introduction to Stan, a highly advanced MCMC tool. I recommend this book highly to anyone who wants to get into the world of Bayesian analysis.

A Student's Guide to Bayesian Statistics gives an excellent introduction to the wonderful world of Bayes. The book is well-suited for students that are new to the topic and do not have a strong mathematical or statistical background. For such students it is one of the best resources on the subject that is currently out there. What I particularly like about the book is that it does not skip the nitty gritty details of Bayesian computation, and that it takes the time to explain important issues on a conceptual level. In this way it covers the basics in a broad range of topics, including key numerical methods. I enjoyed teaching from this book.

This text provides an excellent non-technical overview of the Bayesian framework for statistical analysis. It covers fundamentals through to sampling methods for estimating posterior distributions. Moreover, several important model classes are introduced. The text was clearly never meant as an in depth review but it is a very good place to start in order to get some intuition.

If you want to get started on Bayesian Stats and find books like Bayesian Data Analysis (Gelman et al) somewhat intimidating for a beginner (or someone from a different STEM background), I recommend you get this book and work on it. I worked on this book cover to cover, worked out all the problems, and watched Dr Lambert's YouTube videos (he also mentions them in the book). I'm fairly comfortable with the basics now, and not only can I venture into Bayesian Data Analysis book, but also put what I learnt into practice in my own research.

What I liked about this book is that the author makes a tremendous effort to really teach you Bayes Statistics. He doesn’t take for granted that you know Bayes statistics as some books do. Besides using a mathematical intuitive approach, the author provides a wealth of material (exercises, videos, interactive simulations). Ah, one more thing, I would dare to say that it’s one of the best introductions to STAN. Do you really want to understand Bayesian Statistics? Then this book is a must.

The paperback version did not have any color diagrams, where it's clear it should have. Also, the text was very light. It almost looks like the copy I received was a bad photo copy.

I accidentally saw this book on Amazon.com and was immediately attracted by the name of each chapter and section in this book; after I bought this book, I was impressed by the real contents in this book while reading. With little math in the book, the author is presenting bayesian statistics conceptually which is awesome and even more difficult than just listing tons of mathematical equations and probability density functions. Even best, there are problem sets at the end of each chapter and online videos and answers to the problems sets for you to learn, practice, and learn again!