  This paper provides a comprehensive tutorial for Bayesian practitioners in
pharmacometrics using Pumas workflows. We start by giving a brief motivation of
Bayesian inference for pharmacometrics highlighting limitations in existing
software that Pumas addresses. We then follow by a description of all the steps
of a standard Bayesian workflow for pharmacometrics using code snippets and
examples. This includes: model definition, prior selection, sampling from the
posterior, prior and posterior simulations and predictions, counter-factual
simulations and predictions, convergence diagnostics, visual predictive checks,
and finally model comparison with cross-validation. Finally, the background and
intuition behind many advanced concepts in Bayesian statistics are explained in
simple language. This includes many important ideas and precautions that users
need to keep in mind when performing Bayesian analysis. Many of the algorithms,
codes, and ideas presented in this paper are highly applicable to clinical
research and statistical learning at large but we chose to focus our
discussions on pharmacometrics in this paper to have a narrower scope in mind
and given the nature of Pumas as a software primarily for pharmacometricians.
