In this chapter we will conclude our exploration of Bayesian networks, moving to the more general case in which each variable in the data is modelled with the random variable that best suits it rather than limiting ourselves to multinomial and normal distributions. For this purpose, we will use the Stan MCMC sampler through its interface rstan. As an example, we will model waiting times in Accidents & Emergency departments using public data from UK's National Health Service.