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An excellent resource on Bayesian analysis accessible to students from a diverse range of statistical backgrounds and interests. Easy to follow with well documented examples to illustrate key concepts.
When I was a grad student, Bayesian statistics was restricted to those with the mathematical fortitude to plough through source literature. Thanks to Lambert, we now have something we can give to the modern generation of nascent data scientists as a first course. Love the supporting videos, too!
Written in highly accessible language, this book is the gateway for students to gain a deep understanding of the logic of Bayesian analysis and to apply that logic with numerous carefully selected hands-on examples. Lambert moves seamlessly from a traditional Bayesian approach (using analytic methods) that serves to solidify fundamental concepts, to a modern Bayesian approach (using computational sampling methods) that endows students with the powerful and practical powers of application.
A balanced combination of theory, application and implementation of Bayesian statistics in a not very technical language. A tangible introduction to intangible concepts of Bayesian statistics for beginners.
The late, famous statistician Jimmie Savage would have taken great pleasure in this book based on his work in the 1960s on Bayesian statistics. He would have marveled at the presentations in the book of many new and strong statistical and computer analyses.
While there is increasing interest in Bayesian statistics among scholars of different social science disciplines, I always looked for a text book which is accessible to a wide range of students who do not necessarily have extended knowledge of statistics. Now, I believe that this is the first textbook of Bayesian statistics, which can also be used for social science undergraduate students. Ben Lambert begins with a general introduction to statistical inference and successfully brings the readers to more specific and practical aspects of Bayesian inference.
This book offers a path to get into the field of Bayesian statistics with no previous knowledge. Building from elementary to advanced topics, including theoretic and computational aspects, and focusing on the application, it is an excellent read for newcomers to the Bayesian world.
This book was used as essential reading throughout my module (on an MSc level) not just for learning the “what”, “why” and “how” about the key principles and theory behind Bayesian statistics; but for understanding the practical component for implementing statistical analysis the Bayesian way using Stan interfaced with RStudio through RStan package, as well as for learning the Stan and RStan coding etiquettes for implementing Bayesian modelling and gaining its mastery in Stan and RStudio.
Hands down the best introduction to Bayesian approaches. Unlike other "introductions", Lambert doesn't assume an acquaintance with integral calculus and helps the student instead to build an intuition about Bayesian approaches (and their distinction from frequentist approaches). I'm sure this will take its place alongside Field's book on SPSS as a must-have for psychology undergraduates and post-graduates.
Probably the best introductory textbook for bayesian statistics. - In particular, it is very applied, provides a modern and up-to-date introduction, as well as clear guides how to best use the book.
very essential has to my lectures
there aren't many students doing Bayesian Statistics analysis in dissertation this year so we don't provide such course unit. This book is a really helpful supplementary material for the students.
A very useful reference with good examples, well-structured and progressive.
Clear and useful guide
