Bayesian analysis is the statistical paradigm that uses probability statements to answer research questions about unknown parameters. A parameter in Bayesian analysis is summarised by an entire distribution of values rather than a single fixed value as in classical frequentist analysis. Estimating this distribution, a posterior distribution of an interesting parameter is central to Bayesian analysis. A posterior distribution comprises a prior distribution and a likelihood model that provides information about the parameter based on observed data. The posterior distribution is analytically or approximated by one of the Markov chain Monte Carlo (MCMC) methods, depending on the prior distribution and likelihood model.