Bayesian Essentials with R 2nd edition/

Abstract

This book provides a practical introduction to Bayesian statistics and computational Bayesian modelling using the R programming language. It explains the fundamental principles of Bayesian inference, including prior distributions, posterior distributions, parameter estimation, and statistical model evaluation. The book covers a range of statistical applications, including normal models, regression analysis, variable selection, generalized linear models, capture–recapture experiments, mixture models, time series analysis, and image analysis. Using real datasets, computational examples, and R programming techniques, it demonstrates how Bayesian methods can be applied to analyse data and solve statistical problems. The book is intended for undergraduate and postgraduate students, researchers, statisticians, and professionals seeking to develop practical skills in Bayesian data analysis.