13 Bayesian statistics
This chapter introduces Bayesian inference in R by deriving posterior distributions as proportional to likelihood × prior, demonstrating grid-based estimation for continuous (normal) and binomial models with one or two parameters, computing joint and marginal posteriors, and illustrating practical applications such as diagnostic test updating via Bayes’ theorem; it also highlights computational scaling challenges and motivates the use of MCMC for higher-dimensional problems.