Until recently, many texts on Bayesian inference assumed the reader had a strong background in mathematics or statistics. I found that really frustrating and it really got in my way of understanding this stuff. But this concise book (~160 pages) is a really great introduction. If I had this book when I was learning, then my journey would have been much easier.
Rather than diving directly into things, Chapter 1 provides a range of examples that demonstrate some of the core concepts. I think this is really important because often people are coming from a frequentist background, and unless certain key conceptual shifts are made, then it’s tricky to gain traction. Chapter 7 (Bayesian Wars) deals with this aspect as well, so I felt it might be better coming after Chapter 1.
I think the topic coverage is great for an introductory book. It will get the reader familiar with the workings of a lot of basic problems/models, which provides an excellent foundation for going on to more elaborate situations such as hierarchical inference or model comparison.
The inclusion of an Appendix of mathematical notation was very useful.
Highly recommended. One of the best introductions to the nuts and bolts of Bayesian inference for beginners.