This chapter discusses the practical implementation of the Bayesian methods for statistical modelling. It reviews the available Bayesian software and highlights how these packages differ in the way interval-censored data should be handled. The chapter deals with parametric Bayesian approaches, which are by far the most popular and explores the choice of a Bayesian summary measure to express the importance of model parameters. It describes the principles that are the basis for the Markov chain Monte Carlo (MCMC) algorithms and also reviews how to select and check assumed models in a Bayesian context. The Bayesian nonparametric and semiparametric approach is of a more advanced theoretical level. A Bayesian analysis depends on the assumed model for the data and the chosen priors. Parametric inference is still very popular in both the frequentist and the Bayesian paradigm. In a Bayesian context it is relatively easy to relax parametric assumptions, partly due to the MCMC software.