A Student’s Guide to Bayesian Statistics

by Ben Lambert

Supported by a wealth of learning features, exercises, and visual elements as well as online video tutorials and interactive simulations, this book is the first student-focused introduction to Bayesian statistics.

Without sacrificing technical integrity for the sake of simplicity, the author draws upon accessible, student-friendly language to provide approachable instruction perfectly aimed at statistics and Bayesian newcomers. Through a logical structure that introduces and builds upon key concepts in a gradual way and slowly acclimatizes students to using R and Stan software, the book covers:

An introduction to probability and Bayesian inference Understanding Bayes′ rule Nuts and bolts of Bayesian analytic methods Computational Bayes and real-world Bayesian analysis Regression analysis and hierarchical methods This unique guide will help students develop the statistical confidence and skills to put the Bayesian formula into practice, from the basic concepts of statistical inference to complex applications of analyses.

Genres: Textbooks, Mathematics, Technology, Nonfiction, Science, Stem

520 pages, Paperback

Published May 4, 2018

Reviews

[Tiny Van · Rating 5 out of 5]

This is an excellent book for those who want to dive in the amazing world of Bayesian statistics. In my view, every scientist (and therefore every student) should actually want to learn this, the Bayesian approach is very close to philosophy of science in the sense that one is forced to think hard about a certain problem: what is known already, what do data tell us, and how can we thus update our knowledge. This is formalized in the Bayesian approach following Bayes' theorem. Ben Lambert has made a tremendous achievement in taking the reader by the hand and explaining step by step the underlying probability theory, and how to apply that to real world problems. He has made a big effort to tell the story as much as possible in a non-mathematical way, and as far as I am concerned, he succeeded in that with great success. It is also very nicely written with a good sense of humour. I recommend this very strongly for anyone who wants to learn more about the Bayesian approach!
Tiny van Boekel, Professor in Food Science, Wageningen University, the Netherlands

[Veronica · Rating 5 out of 5]

this is what I am currently reading in parallel to "statistical rethinking" which got better ratings so I had high hope for that one but turns out this one is of much more help to me. Like the title suggested, this is a book intended for students, not experts already familiar with the topic, so I don't really think it's fair for some readers to rate it low just because they find it too easy... If you are a beginner, new to Bayesian stats, like me, then this is definitely a highly recommended one! I find the explanations and logic clear, and the extra explanations here and there that may be deemed "too easy" for more experienced readers are actually very reassuring to me. I got confirmation from these small details knowing that I did understand it. I am at chapter 12 now (so much further along than the McElreath book), and so far so good. I've heard that Part V is pretty advanced so hopefully I could get through that sooner than later.

[Henry · Rating 3 out of 5]

Written in an informal style, and up-to-date with the latest techniques in use for most Bayesian statistics (at least if you're an R user). However this isn't a particularly rigorous or mathematical book; it's not intended to be. The author makes frequent reference to Gelman et al and the most famous book in the field, Bayesian Data Analysis. I think for many people that will be a better choice to learn these techniques. But still I appreciate the effort to bring Bayesian statistics to a wider audience.

[Amanuel Anteneh · Rating 5 out of 5]

I used this book because the lectures for my Bayesian stats course were not great. I'm glad I did because this is the most lucid textbook I have ever read. The book is mainly on Bayesian statistics but Ben also goes over the necessary Frequentist concepts as needed and explains those just as clearly. He also goes over basic concepts in statistical modeling. I really wish more textbooks for students were this good.

[Jilong Wu · Rating 5 out of 5]

I know it could be imperfect (Of course, there isn't a so-called perfect book ), but it indeed helps me pick up the courage to start my adventure in the Bayesian Statistics world. Particularly, considering the efforts that the author has done to make so many videos on YouTube, I would like to give lots of respect to the author.

[Ben]

429/682

[Ariyasacca · Rating 5 out of 5]

Exactly what I looked for. Should have been published earlier.

[Brian Fang · Rating 3 out of 5]

Very lucid writing style and a joy to read. I especially enjoyed the little pieces of historical context.