  The aim of this work is the comparison of the performance of the three
popular software platforms JAGS, NIMBLE and Stan. These probabilistic
programming languages are able to automatically generate samples from the
posterior distribution of interest using MCMC algorithms, starting from the
specification of a Bayesian model, i.e. the likelihood and the prior. The final
goal is to present a detailed analysis of their strengths and weaknesses to
statisticians or applied scientists. In this way, we wish to contribute to make
them fully aware of the pros and cons of this software. We carry out a
systematic comparison of the three platforms on a wide class of models, prior
distributions, and data generating mechanisms. Our extensive simulation studies
evaluate the quality of the MCMC chains produced, the efficiency of the
software and the goodness of fit of the output. We also consider the efficiency
of the parallelization made by the three platforms.
