A Student’s Guide to Bayesian Statistics
- Ben Lambert - University of Oxford, United Kingdom
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.
Supplements
https://study.sagepub.com/lambert
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.
This is an excellent book, which is excellent for students who have been exposed to statistics (e.g., at least GLM, hierarchical regression etc.).
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
