The book introduces all the important topics that one would usually cover in a beginning graduate level class on Bayesian biostatistics. The careful introduction of the Bayesian viewpoint and the mechanics of implementing Bayesian inference in the early chapters makes the it a complete self contained introduction to Bayesian inference for biomedical problems. As a natural consequence of the biostatistics target audience all methods and discussions are well motivated by specific inference problems as they arise in biomedical research. Even without this target audience in mind, the same motivating problems would be a great pedagogical choice to keep discussion focused and to make many modeling and inference choices intuitively appealing. Overall the authors have made well informed choices about including material and topics, and about the level of details of some of the formal presentation, fittingly leaving some details to references. Another great feature for using this book as a textbook is the inclusion of extensive problem sets, going well beyond construed and simple problems. Many exercises consider real data and studies, providing very useful examples in addition to serving as problems. In summary, the book is a great introduction to Bayesian inference for readers with an interest in biomedical applications, but who do not necessarily have a formal biostatistics background.