A Student’s Guide to Bayesian Statistics

As a bioinformatician specializing in applied statistics, I found this book to be an excellent introduction to Bayesian Statistics for students and researchers with a non-mathematical background. The concepts and ideas are introduced with exceptional clarity. For those aiming to learn more about Bayesian statistics on their own, this and McElreath’s book will be essential stepping stones towards generating an understanding of more advanced textbooks and modern literature in applied Bayesian statistics. Top aspects: + Clear pedagogical text layout; concise introductions and chapter summaries + Good coverage of topics relevant to applied Bayesian analysis work (probability theory interpretations, distributions, model evaluation, model fitting algorithms, hierarchical & regression modeling ) + Excellent introduction to Bayesian model fitting algorithms (Metropolis Hastings, Gibbs and HMC) from a conceptual angle + Excellent example figures + Brief introductory chapter on stan for those who haven’t used it before It is worth noting that the book has rather few applied statistics examples; the focus of the book clearly lies more on explaining the theory. On this end McElreath’s, Krushke’s and Hilbe’s books are more extensive and contain more examples with stan code. For those wanting to get started with Bayesian statistics I thus recommend getting one of these references and their corresponding R & stan code as a supplement to Lambert's book. I own both a hardcopy and digital copy of the book; the digital version (google play) is overall quite good but for some reason contains rather blurry pictures and weirdly formatted equations. The hardcopy version has excellent resolution figures and nicely formatted equations. I highly recommend getting a hardcopy of this book.

Bayesian Statistics is very difficult, if not the hardest, statistics subject to master. Unlike machine learning / time series / discrete data analysis, Bayesian Statistics require excellent understanding in probability and statistical theory to start with. And after that, you are required to understand a wide range of topics in order to just touch the surface on Bayesian Statistics. For a non-math/statistics major student, there is literally no way to start. But this book really helps you to pick up the intuition and the math behind every single logic in Bayesian Statistics, walks through the process as a mathematician in the most plain and comprehensive way, from the basic of bayes rule to the state-of-the-art technique like MCMC. I would 10/10 recommend this book to those who studied in computer science / finance and just want to boost up your understanding in both statistics and Bayesian approach.

説明が丁寧で入門書としては最適だいと思います

I needed a refresher on Bayesian stats. While browsing/watching related youtube videos I have stumbled upon the author's channel. This is how I learned about 'A Student's Guide to Bayesian Statistics'. In retrospect I cannot believe this was such a random sequence of events, since the "Student's Guide' has now become my personal favourite text book I ever had. The material is presented in a very clear way, it builds up from simple examples to more complicated ones. To go one step further the book offers a few pretty advanced problems to work out (there are answers/solutions available on-line too on the author's web-site). In theory all the text books should be like that, but in practice it is not all that frequent, especially when it comes to any sort of applied math. It was important for me that the text has no insane logic gaps along the lines of 'now, obviously' on which I tend to hang up particularly badly. The illustrations are fantastic too. I feel if the author ever decides to write another book on any subject even remotely relevant to my fields of interest - I will buy it without hesitation.

El libro es muy claro, en un tema poco intuitivo, con buenos ejemplos de la vida real vista desde la perspectiva bayesiana. Para quien quiera adentrarse en la estadística de Bayes desde el principio, esta muy bien. Pero quería comentar que por fin, un libro que verdaderamente da lo que promete: recursos on line del libro, muy completos. Hay videos explicativos de todos los capítulos, y conceptos. Los problemas están todos resueltos. No se suele encontrarse tanto apoyo al lector. Lo recomiendo mucho.